Computer vision Books
Elsevier Science Computer Vision
Book SynopsisTable of Contents1. Vision, the Challenge2. Images and Imaging Operations3. Image Filtering and Morphology4. The Role of Thresholding5. Edge Detection6. Corner, Interest Point and Invariant Feature Detection7. Texture Analysis8. Binary Shape Analysis9. Boundary Pattern Analysis10. Line, Circle and Ellipse Detection11. The Generalised Hough Transform12. Object Segmentation and Shape Models13. Basic Classification Concepts14. Machine Learning: Probabilistic Methods15. Deep Learning Networks16. The Three-Dimensional World17. Tackling the Perspective n-point Problem18. Invariants and perspective19. Image transformations and camera calibration20. Motion21. Face Detection and Recognition: the Impact of Deep Learning22. Surveillance23. In-Vehicle Vision Systems24. Epilogue—Perspectives in VisionAppendix A: Robust statisticsAppendix B: The Sampling TheoremAppendix C: The representation of colourAppendix D: Sampling from distributions
£77.39
Pearson Education Computer Vision A Modern Approach
Book SynopsisTable of ContentsI IMAGE FORMATION 1 1 Geometric Camera Models 3 1.1 Image Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 Pinhole Perspective . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2 Weak Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1.3 Cameras with Lenses . . . . . . . . . . . . . . . . . . . . . . . 8 1.1.4 The Human Eye . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.2 Intrinsic and Extrinsic Parameters . . . . . . . . . . . . . . . . . . . 14 1.2.1 Rigid Transformations and Homogeneous Coordinates . . . . 14 1.2.2 Intrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 16 1.2.3 Extrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 18 1.2.4 Perspective Projection Matrices . . . . . . . . . . . . . . . . . 19 1.2.5 Weak-Perspective Projection Matrices . . . . . . . . . . . . . 20 1.3 Geometric Camera Calibration . . . . . . . . . . . . . . . . . . . . . 22 1.3.1 ALinear Approach to Camera Calibration . . . . . . . . . . . 23 1.3.2 ANonlinear Approach to Camera Calibration . . . . . . . . . 27 1.4 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2 Light and Shading 32 2.1 Modelling Pixel Brightness . . . . . . . . . . . . . . . . . . . . . . . 32 2.1.1 Reflection at Surfaces . . . . . . . . . . . . . . . . . . . . . . 33 2.1.2 Sources and Their Effects . . . . . . . . . . . . . . . . . . . . 34 2.1.3 The Lambertian+Specular Model . . . . . . . . . . . . . . . . 36 2.1.4 Area Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2 Inference from Shading . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.2.1 Radiometric Calibration and High Dynamic Range Images . . 38 2.2.2 The Shape of Specularities . . . . . . . . . . . . . . . . . . . 40 2.2.3 Inferring Lightness and Illumination . . . . . . . . . . . . . . 43 2.2.4 Photometric Stereo: Shape from Multiple Shaded Images . . 46 2.3 Modelling Interreflection . . . . . . . . . . . . . . . . . . . . . . . . . 52 2.3.1 The Illumination at a Patch Due to an Area Source . . . . . 52 2.3.2 Radiosity and Exitance . . . . . . . . . . . . . . . . . . . . . 54 2.3.3 An Interreflection Model . . . . . . . . . . . . . . . . . . . . . 55 2.3.4 Qualitative Properties of Interreflections . . . . . . . . . . . . 56 2.4 Shape from One Shaded Image . . . . . . . . . . . . . . . . . . . . . 59 2.5 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3 Color 68 3.1 Human Color Perception . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.1.1 Color Matching . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.1.2 Color Receptors . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.2 The Physics of Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.2.1 The Color of Light Sources . . . . . . . . . . . . . . . . . . . 73 3.2.2 The Color of Surfaces . . . . . . . . . . . . . . . . . . . . . . 76 3.3 Representing Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 3.3.1 Linear Color Spaces . . . . . . . . . . . . . . . . . . . . . . . 77 3.3.2 Non-linear Color Spaces . . . . . . . . . . . . . . . . . . . . . 83 3.4 AModel of Image Color . . . . . . . . . . . . . . . . . . . . . . . . . 86 3.4.1 The Diffuse Term . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.4.2 The Specular Term . . . . . . . . . . . . . . . . . . . . . . . . 90 3.5 Inference from Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 3.5.1 Finding Specularities Using Color . . . . . . . . . . . . . . . 90 3.5.2 Shadow Removal Using Color . . . . . . . . . . . . . . . . . . 92 3.5.3 Color Constancy: Surface Color from Image Color . . . . . . 95 3.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 II EARLY VISION: JUST ONE IMAGE 105 4 Linear Filter
£69.34
Springer Nature Switzerland AG Computer Vision: Algorithms and Applications
Book SynopsisComputer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos.More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles.Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.Table of Contents
£61.74
World Scientific Publishing Co Pte Ltd Linear Algebra And Optimization With Applications
Book SynopsisThis book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.
£81.00
World Scientific Publishing Co Pte Ltd Computational Geometry With Independent And
Book SynopsisThis comprehensive compendium describes a parametric model and algorithmic theory to represent geometric entities with dependent uncertainties between them. The theory, named Linear Parametric Geometric Uncertainty Model (LPGUM), is an expressive and computationally efficient framework that allows to systematically study geometric uncertainty and its related algorithms in computer geometry.The self-contained monograph is of great scientific, technical, and economic importance as geometric uncertainty is ubiquitous in mechanical CAD/CAM, robotics, computer vision, wireless networks and many other fields. Geometric models, in contrast, are usually exact and do not account for these inaccuracies.This useful reference text benefits academics, researchers, and practitioners in computer science, robotics, mechanical engineering and related fields.
£63.00
O'Reilly Media AI at the Edge
Book SynopsisThis practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI.
£47.99
Springer-Verlag New York Inc. Introduction to Biometrics
Book SynopsisIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.Table of ContentsIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.
£59.99
John Wiley & Sons Inc Object Detection by Stereo Vision Images
Book SynopsisOBJECT DETECTION BY STEREO VISION IMAGES Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers. Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages,Table of ContentsPreface xiii 1 Data Conditioning for Medical Imaging 1 Shahzia Sayyad, Deepti Nikumbh, Dhruvi Lalit Jain, Prachi Dhiren Khatri, Alok Saratchandra Panda and Rupesh Ravindra Joshi 1.1 Introduction 2 1.2 Importance of Image Preprocessing 2 1.3 Introduction to Digital Medical Imaging 3 1.3.1 Types of Medical Images for Screening 4 1.3.1.1 X-rays 4 1.3.1.2 Computed Tomography (CT) Scan 4 1.3.1.3 Ultrasound 4 1.3.1.4 Magnetic Resonance Imaging (MRI) 5 1.3.1.5 Positron Emission Tomography (PET) Scan 5 1.3.1.6 Mammogram 5 1.3.1.7 Fluoroscopy 5 1.3.1.8 Infrared Thermography 6 1.4 Preprocessing Techniques of Medical Imaging Using Python 6 1.4.1 Medical Image Preprocessing 6 1.4.1.1 Reading the Image 7 1.4.1.2 Resizing the Image 7 1.4.1.3 Noise Removal 8 1.4.1.4 Filtering and Smoothing 9 1.4.1.5 Image Segmentation 11 1.5 Medical Image Processing Using Python 13 1.5.1 Medical Image Processing Methods 16 1.5.1.1 Image Formation 17 1.5.1.2 Image Enhancement 19 1.5.1.3 Image Analysis 19 1.5.1.4 Image Visualization 19 1.5.1.5 Image Management 19 1.6 Feature Extraction Using Python 20 1.7 Case Study on Throat Cancer 24 1.7.1 Introduction 24 1.7.1.1 HSI System 25 1.7.1.2 The Adaptive Deep Learning Method Proposed 25 1.7.2 Results and Findings 27 1.7.3 Discussion 28 1.7.4 Conclusion 29 1.8 Conclusion 29 References 30 Additional Reading 31 Key Terms and Definition 32 2 Detection of Pneumonia Using Machine Learning and Deep Learning Techniques: An Analytical Study 33 Shravani Nimbolkar, Anuradha Thakare, Subhradeep Mitra, Omkar Biranje and Anant Sutar 2.1 Introduction 33 2.2 Literature Review 35 2.3 Learning Methods 41 2.3.1 Machine Learning 41 2.3.2 Deep Learning 42 2.3.3 Transfer Learning 42 2.4 Detection of Lung Diseases Using Machine Learning and Deep Learning Techniques 43 2.4.1 Dataset Description 43 2.4.2 Evaluation Platform 44 2.4.3 Training Process 44 2.4.4 Model Evaluation of CNN Classifier 46 2.4.5 Mathematical Model 47 2.4.6 Parameter Optimization 47 2.4.7 Performance Metrics 50 2.5 Conclusion 52 References 53 3 Contamination Monitoring System Using IOT and GIS 57 Kavita R. Singh, Ravi Wasalwar, Ajit Dharmik and Deepshikha Tiwari 3.1 Introduction 58 3.2 Literature Survey 58 3.3 Proposed Work 60 3.4 Experimentation and Results 61 3.4.1 Experimental Setup 61 3.5 Results 64 3.6 Conclusion 70 Acknowledgement 71 References 71 4 Video Error Concealment Using Particle Swarm Optimization 73 Rajani P. K. and Arti Khaparde 4.1 Introduction 74 4.2 Proposed Research Work Overview 75 4.3 Error Detection 75 4.4 Frame Replacement Video Error Concealment Algorithm 77 4.5 Research Methodology 77 4.5.1 Particle Swarm Optimization 78 4.5.2 Spatio-Temporal Video Error Concealment Method 78 4.5.3 Proposed Modified Particle Swarm Optimization Algorithm 79 4.6 Results and Analysis 83 4.6.1 Single Frame With Block Error Analysis 85 4.6.2 Single Frame With Random Error Analysis 86 4.6.3 Multiple Frame Error Analysis 88 4.6.4 Sequential Frame Error Analysis 91 4.6.5 Subjective Video Quality Analysis for Color Videos 93 4.6.6 Scene Change of Videos 94 4.7 Conclusion 95 4.8 Future Scope 97 References 97 5 Enhanced Image Fusion with Guided Filters 99 Nalini Jagtap and Sudeep D. Thepade 5.1 Introduction 100 5.2 Related Works 100 5.3 Proposed Methodology 102 5.3.1 System Model 102 5.3.2 Steps of the Proposed Methodology 104 5.4 Experimental Results 104 5.4.1 Entropy 104 5.4.2 Peak Signal-to-Noise Ratio 105 5.4.3 Root Mean Square Error 107 5.4.3.1 Qab/f 108 5.5 Conclusion 108 References 109 6 Deepfake Detection Using LSTM-Based Neural Network 111 Tejaswini Yesugade, Shrikant Kokate, Sarjana Patil, Ritik Varma and Sejal Pawar 6.1 Introduction 111 6.2 Related Work 112 6.2.1 Deepfake Generation 112 6.2.2 LSTM and CNN 112 6.3 Existing System 113 6.3.1 AI-Generated Fake Face Videos by Detecting Eye Blinking 113 6.3.2 Detection Using Inconsistence in Head Pose 113 6.3.3 Exploiting Visual Artifacts 113 6.4 Proposed System 114 6.4.1 Dataset 114 6.4.2 Preprocessing 114 6.4.3 Model 115 6.5 Results 117 6.6 Limitations 119 6.7 Application 119 6.8 Conclusion 119 References 119 7 Classification of Fetal Brain Abnormalities with MRI Images: A Survey 121 Kavita Shinde and Anuradha Thakare 7.1 Introduction 121 7.2 Related Work 123 7.3 Evaluation of Related Research 129 7.4 General Framework for Fetal Brain Abnormality Classification 129 7.4.1 Image Acquisition 130 7.4.2 Image Pre-Processing 130 7.4.2.1 Image Thresholding 130 7.4.2.2 Morphological Operations 131 7.4.2.3 Hole Filling and Mask Generation 131 7.4.2.4 MRI Segmentation for Fetal Brain Extraction 132 7.4.3 Feature Extraction 132 7.4.3.1 Gray-Level Co-Occurrence Matrix 133 7.4.3.2 Discrete Wavelet Transformation 133 7.4.3.3 Gabor Filters 134 7.4.3.4 Discrete Statistical Descriptive Features 134 7.4.4 Feature Reduction 134 7.4.4.1 Principal Component Analysis 135 7.4.4.2 Linear Discriminant Analysis 136 7.4.4.3 Non-Linear Dimensionality Reduction Techniques 137 7.4.5 Classification by Using Machine Learning Classifiers 137 7.4.5.1 Support Vector Machine 138 7.4.5.2 K-Nearest Neighbors 138 7.4.5.3 Random Forest 139 7.4.5.4 Linear Discriminant Analysis 139 7.4.5.5 Naïve Bayes 139 7.4.5.6 Decision Tree (DT) 140 7.4.5.7 Convolutional Neural Network 140 7.5 Performance Metrics for Research in Fetal Brain Analysis 141 7.6 Challenges 142 7.7 Conclusion and Future Works 142 References 143 8 Analysis of COVID-19 Data Using Machine Learning Algorithm 147 Chinnaiah Kotadi, Mithun Chakravarthi K., Srihari Chintha and Kapil Gupta 8.1 Introduction 147 8.2 Pre-Processing 148 8.3 Selecting Features 149 8.4 Analysis of COVID-19–Confirmed Cases in India 152 8.4.1 Analysis to Highest COVID-19–Confirmed Case States in India 153 8.4.2 Analysis to Highest COVID-19 Death Rate States in India 153 8.4.3 Analysis to Highest COVID-19 Cured Case States in India 154 8.4.4 Analysis of Daily COVID-19 Cases in Maharashtra State 155 8.5 Linear Regression Used for Predicting Daily Wise COVID- 19 Cases in Maharashtra 156 8.6 Conclusion 157 References 157 9 Intelligent Recommendation System to Evaluate Teaching Faculty Performance Using Adaptive Collaborative Filtering 159 Manish Sharma and Rutuja Deshmukh 9.1 Introduction 160 9.2 Related Work 162 9.3 Recommender Systems and Collaborative Filtering 164 9.4 Proposed Methodology 165 9.5 Experiment Analysis 167 9.6 Conclusion 168 References 168 10 Virtual Moratorium System 171 Manisha Bhende, Muzasarali Badger, Pranish Kumbhar, Vedanti Bhatkar and Payal Chavan 10.1 Introduction 172 10.1.1 Objectives 172 10.2 Literature Survey 172 10.2.1 Virtual Assistant—BLU 172 10.2.2 HDFC Ask EVA 173 10.3 Methodologies of Problem Solving 173 10.4 Modules 174 10.4.1 Chatbot 174 10.4.2 Android Application 175 10.4.3 Web Application 175 10.5 Detailed Flow of Proposed Work 176 10.5.1 System Architecture 176 10.5.2 DFD Level 1 177 10.6 Architecture Design 178 10.6.1 Main Server 178 10.6.2 Chatbot 178 10.6.3 Database Architecture 180 10.6.4 Web Scraper 180 10.7 Algorithms Used 181 10.7.1 AES-256 Algorithm 181 10.7.2 Rasa NLU 181 10.8 Results 182 10.9 Discussions 183 10.9.1 Applications 183 10.9.2 Future Work 183 10.9.3 Conclusion 183 References 183 11 Efficient Land Cover Classification for Urban Planning 185 Vandana Tulshidas Chavan and Sanjeev J. Wagh 11.1 Introduction 185 11.2 Literature Survey 189 11.3 Proposed Methodology 191 11.4 Conclusion 192 References 192 12 Data-Driven Approches for Fake News Detection on Social Media Platforms: Review 195 Pradnya Patil and Sanjeev J. Wagh 12.1 Introduction 196 12.2 Literature Survey 196 12.3 Problem Statement and Objectives 201 12.3.1 Problem Statement 201 12.3.2 Objectives 201 12.4 Proposed Methodology 202 12.4.1 Pre-Processing 202 12.4.2 Feature Extraction 203 12.4.3 Classification 203 12.5 Conclusion 204 References 204 13 Distance Measurement for Object Detection for Automotive Applications Using 3D Density-Based Clustering 207 Anupama Patil, Manisha Bhende, Suvarna Patil and P. P. Shevatekar 13.1 Introduction 208 13.2 Related Work 210 13.3 Distance Measurement Using Stereo Vision 213 13.3.1 Calibration of the Camera 215 13.3.2 Stereo Image Rectification 215 13.3.3 Disparity Estimation and Stereo Matching 216 13.3.4 Measurement of Distance 217 13.4 Object Segmentation in Depth Map 218 13.4.1 Formation of Depth Map 218 13.4.2 Density-Based in 3D Object Grouping Clustering 218 13.4.3 Layered Images Object Segmentation 219 13.4.3.1 Image Layer Formation 221 13.4.3.2 Determination of Object Boundaries 222 13.5 Conclusion 223 References 224 14 Real-Time Depth Estimation Using BLOB Detection/ Contour Detection 227 Arokia Priya Charles, Anupama V. Patil and Sunil Dambhare 14.1 Introduction 227 14.2 Estimation of Depth Using Blob Detection 229 14.2.1 Grayscale Conversion 230 14.2.2 Thresholding 231 14.2.3 Image Subtraction in Case of Input with Background 232 14.2.3.1 Preliminaries 233 14.2.3.2 Computing Time 234 14.3 Blob 234 14.3.1 BLOB Extraction 234 14.3.2 Blob Classification 235 14.3.2.1 Image Moments 236 14.3.2.2 Centroid Using Image Moments 238 14.3.2.3 Central Moments 238 14.4 Challenges 241 14.5 Experimental Results 241 14.6 Conclusion 251 References 255 Index 257
£118.40
O'Reilly Media Learning OpenCV 3
Book SynopsisGet started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists.
£50.99
Springer Nature Switzerland AG Computer Vision – ACCV 2020: 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part VI
Book SynopsisThe six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.*The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually.Table of ContentsApplications of Computer Vision, Vision for X.- Query by Strings and Return Ranking Word Regions with Only One Look.- Single-Image Camera Response Function Using Prediction Consistency and Gradual Refinement.- FootNet: An efficient convolutional network for multiview 3D foot reconstruction.- Synthetic-to-real domain adaptation for lane detection.- RAF-AU Database: In-the-Wild Facial Expressions with Subjective Emotion Judgement and Objective AU Annotations.- DoFNet: Depth of Field Difference Learning for Detecting Image Forgery.- Explaining image classifiers by removing input features using generative models.- Do We Need Sound for Sound Source Localization?.- Modular Graph Attention Network for Complex Visual Relational Reasoning.- CloTH-VTON: Clothing Three-dimensional reconstruction for Hybrid image-based Virtual Try-ON.- Multi-label X-ray Imagery Classification via Bottom-up Attention and Meta Fusion.- Learning End-to-End Action Interaction by Paired-Embedding Data Augmentation.- Sketch-to-Art: Synthesizing Stylized Art Images From Sketches.- Road Obstacle Detection Method Based on an Autoencoder with Semantic Segmentation.- SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection.- Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion.- Audiovisual Transformer with Instance Attention for Audio-Visual Event Localization.- Watch, read and lookup: learning to spot signs from multiple supervisors.- Domain-transferred Face Augmentation Network.- Pose Correction Algorithm for Relative Frames between Keyframes in SLAM.- Dense-Scale Feature Learning in Person Re-Identification.- Class-incremental Learning with Rectified Feature-Graph Preservation.- Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation.- Towards Robust Fine-grained Recognition by Maximal Separation of Discriminative Features.- Visually Guided Sound Source Separation using Cascaded Opponent Filter Network.- Channel Recurrent Attention Networks for Video Pedestrian Retrieval.- In Defense of LSTMs for Addressing Multiple Instance Learning Problems.- Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score.- Show, Conceive and Tell: Image Captioning with Prospective Linguistic Information.- Datasets and Performance Analysis.- RGB-T Crowd Counting from Drone: A Benchmark and MMCCN Network.- Webly Supervised Semantic Embeddings for Large Scale Zero-Shot Learning.- Compensating for the Lack of Extra Training Data by Learning Extra Representation.- Class-Wise Difficulty-Balanced Loss for Solving Class-Imbalance.- OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets.- Pre-training without Natural Images.- TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines.- A Day on Campus - An Anomaly Detection Dataset for Events in a Single Camera.- A Benchmark and Baseline for Language-Driven Image Editing.- Self-supervised Learning of Orc-Bert Augmentator for Recognizing Few-Shot Oracle Characters.- Understanding Motion in Sign Language: A New Structured Translation Dataset.- FreezeNet: Full Performance by Reduced Storage Costs.
£80.99
O'Reilly Media Generative Deep Learning
Book SynopsisGenerative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch
£47.99
Elsevier Science Machine Learning for Biomedical Applications
Book SynopsisTable of Contents1. Programming in Python 2. Machine Learning Basics 3. Regression 4. Classification 5. Dimensionality reduction 6. Clustering 7. Ensemble methods 8. Feature extraction and selection 9. Introduction to Deep Learning 10. Neural Networks 11. Convolutional Neural Networks
£55.05
Institute of Physics Publishing Mastering Computer Vision with PyTorch and
Book Synopsis
£71.25
Springer Nature Switzerland AG Collaboration Technologies and Social Computing: 25th International Conference, CRIWG+CollabTech 2019, Kyoto, Japan, September 4–6, 2019, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 25th International Conference, CRIWG+CollabTech 2019, held in Kyoto, Japan in September 2019. The 12 full papers presented in this book together with 8 work-in-progress papers were carefully reviewed and selected from 28 submissions, and the program also included an invited talk. This year presented a merger of the CRIWG and CollabTech conferences after having been jointly held since 2014. The papers published in this proceedings focus on innovative collaboration technologies and social computing.Table of ContentsAwareness of Complementary Knowledge in CSCL: Impact on Learners’ Knowledge Exchange in Small Groups.- Identifying Socio-Technical Means to Support Small Loosely Coupled Groups of Volunteers.- The analysis of collaborative science learning with simulations through dual eye-tracking techniques.- Hybrid Meetings in the Modern Workplace: Stories of Success and Failure.- A CSCL script for supporting moral reasoning in the ethics classroom.- Tailorable Remote Assistance with RemoteAssistKit: A Study of and Design Response to Remote Assistance in the Manufacturing Industry.- Vision-Based Indoor Positioning (VBIP) - An Indoor AR Navigation System With A Virtual Tour Guide.- Developing Hyper-stories in the Context of Cultural Heritage Appreciation.- A Method for Automated Detection of Cultural Difference.- FootstepsMixer: a Tool to Express Multiple People’s Footsteps in a Footstep Transmission System for Awareness Support.- Speech Speed Awareness System Slows Down Native Speaker’s Talk.- A Comic-style Chat System with Japanese Expression Techniques for More Expressive Communication.- Modeling of non-verbal behaviors of students in cooperative learning by using OpenPose.- Implementing a Serious Game to Improve Communication and Social Skills for Children with Autism.- DiAna-AD: Dialog Analysis for Adjusting Duration during Face-to-face Collaborative Discussion.- Group Dynamics in Gameful Collaborative Innovation Processes.- Discovering Latent Country Words: A Step towards Cross-cultural Emotional Communication.- Evaluation of a campus navigation application using an AR character guide.- An Automated Structural Approach to Support Theatrical Performances by Introducing Gesture Recognition to a Cuing System.- Proposal of Emphasized Pseudo Expression for Improving the Recognition of the Presence and Contribution of Remote Participants in Cooperative Work.
£44.99
Springer Nature Switzerland AG Image Analysis and Processing – ICIAP 2019: 20th International Conference, Trento, Italy, September 9–13, 2019, Proceedings, Part I
Book SynopsisThe two-volume set LNCS 11751 and 11752 constitutes the refereed proceedings of the 20th International Conference on Image Analysis and Processing, ICIAP 2019, held in Trento, Italy, in September 2019. The 117 papers presented were carefully reviewed and selected from 207 submissions. The papers cover both classic and the most recent trends in image processing, computer vision, and pattern recognition, addressing both theoretical and applicative aspects. They are organized in the following topical sections: Video Analysis and Understanding; Pattern Recognition and Machine Learning; Deep Learning; Multiview Geometry and 3D Computer Vision; Image Analysis, Detection and Recognition; Multimedia; Biomedical and Assistive Technology; Digital Forensics; Image processing for Cultural Heritage.Table of ContentsVideo Analysis and Understanding.- Pattern Recognition and Machine Learning.- Deep Learning.- Multiview Geometry and 3D Computer Vision.- Image Analysis, Detection and Recognition.- Multimedia.- Biomedical and Assistive Technology.- Digital Forensics.- Image processing for Cultural Heritage.
£62.99
Springer Nature Switzerland AG Intelligent Wavelet Based Techniques for Advanced Multimedia Applications
a huge range and FREE tracked UK delivery on ALL orders.
£80.99
Springer Nature Switzerland AG Computer Vision: Algorithms and Applications
Book SynopsisComputer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos.More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles.Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.Table of Contents
£49.49
Springer Nature Switzerland AG Intelligence Science and Big Data Engineering. Visual Data Engineering: 9th International Conference, IScIDE 2019, Nanjing, China, October 17–20, 2019, Proceedings, Part I
Book SynopsisThe two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019. The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.
£62.99
Springer Nature Switzerland AG Unsupervised Learning in Space and Time: A Modern
Book SynopsisThis book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts. Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way. Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines. Table of Contents1. Unsupervised Visual Learning: from Pixels to Seeing.- 2. Unsupervised Learning of Graph and Hypergraph Matching.- 3. Unsupervised Learning of Graph and Hypergraph Clustering.- 4. Feature Selection meets Unsupervised Learning.- 5. Unsupervised Learning of Object Segmentation in Video with Highly Probable Positive Features.- 6. Coupling Appearance and Motion: Unsupervised Clustering for Object Segmentation through Space and Time.- 7. Unsupervised Learning in Space and Time over Several Generations of Teacher and Student Networks.- 8. Unsupervised Learning Towards the Future.
£113.99
Springer Nature Switzerland AG Computer Vision Systems: 13th International Conference, ICVS 2021, Virtual Event, September 22-24, 2021, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 13th International Conference on Computer Vision Systems, ICVS 2021, held in September 2021. Due to COVID-19 pandemic the conference was held virtually. The 20 papers presented were carefully reviewed and selected from 29 submissions. cover a broad spectrum of issues falling under the wider scope of computer vision in real-world applications, including among others, vision systems for robotics, autonomous vehicles, agriculture and medicine. In this volume, the papers are organized into the sections: attention systems; classification and detection; semantic interpretation; video and motion analysis; computer vision systems in agriculture.Table of ContentsAttention Systems.- Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive Fields.- MARL: Multimodal Attentional Representation Learning for Disease Prediction.- Object Localization with Attribute Preference based on Top-Down Attention.- See the silence: improving visual-only voice activity detection by optical flow and RGB fusion.- Classification and Detection.- Score to Learn: a Comparative Analysis of Scoring Functions for Active Learning in Robotics.- Enhancing the performance of image classification through features automatically learned from depth-maps.- Object Detection on TPU Accelerated Embedded Devices.- Tackling Inter-Class Similarity and Intra-Class Variance for Microscopic Image-based Classification.- Semantic Interpretation.- Measuring the Sim2Real gap in 3D Object classification for different 3D data representation.- Spatially-Constrained Semantic Segmentation with Topological Μaps and Visual Εmbeddings.- Knowledge-enabled generation of semantically annotated image sequences of manipulation activities from VR demonstrations.- Make It Easier: An Empirical Simplification of a Deep 3D Segmentation Network for Human Body Parts.- Video and Motion Analysis.- Video Popularity Prediction through Fusing Early Viewership with Video Content.- Action Prediction during Human-Object Interaction based on DTW and Early Fusion of Human and Object Representations.- GridTrack: Detection and Tracking of Multiple Objects in Dynamic Occupancy Grids.- An Efficient Video Desnowing and Deraining Method with a Novel Variant Dataset.- Computer Vision Systems in Agriculture.- Robust Counting of Soft Fruit through Occlusions with Re-identification.- Non-destructive Soft Fruit Mass and Volume Estimation for Phenotyping in Horticulture.- Learning Image-based Contaminant Detection in Wool Fleece from Noisy Annotations.- Active Learning for Crop-Weed Discrimination by Image Classification from Convolutional Neural Network’s Feature Pyramid Levels.
£49.49
Springer Nature Switzerland AG Computer Analysis of Images and Patterns: 19th International Conference, CAIP 2021, Virtual Event, September 28–30, 2021, Proceedings, Part I
Book SynopsisThe two volume set LNCS 13052 and 13053 constitutes the refereed proceedings of the 19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021, held virtually, in September 2021. The 87 papers presented were carefully reviewed and selected from 129 submissions. The papers are organized in the following topical sections across the 2 volumes: 3D vision, biomedical image and pattern analysis; machine learning; feature extractions; object recognition; face and gesture, guess the age contest, biometrics, cryptography and security; and segmentation and image restoration.Table of Contents3D Vision.- Simultaneous Bi-Directional Structured Light Encoding for Practical Uncalibrated Profilometry.- Joint Global ICP for Improved Automatic Alignment of Full Turn Object Scans.- Fast Projector-Driven Structured Light Matching in Sub-Pixel Accuracy using Bilinear Interpolation Assumption.- Pyramidal Layered Scene Inference with Image Outpainting for Monocular View Synthesis.- Out of the Box: Embodied Navigation in the Real World.-Toward a novel LSB-based collusion-secure fingerprinting schema for 3D video.- A Combinatorial Coordinate System for the Vertices in the Octagonal C4C8 ( R) Grid.- Bilingual Speech Recognition by Estimating Speaker Geometry from Video Data.- Cost-efficient Color Correction Approach on Uncontrolled Lighting Conditions.- HPA-Net: Hierarchical and Parallel Aggregation Network for Context Learning in Stereo Matching.- MTStereo 2.0: accurate stereo depth estimation via Max-tree matching.- Biomedical Image and Pattern Analysis.- H-OCS: a hybrid optic cup segmentation of retinal images.- Retinal Vessel Segmentation using Blending-based Conditional Generative Adversarial Networks.- U-shaped densely connected Convolutions for Left ventricle segmentation from CMR images.- Deep Learning approaches for Head and Operculum Segmentation in Zebrafish Microscopy Images.- Shape Analysis Approach towards Assessment of Cleft Lip Repair Outcome.- MMEC: Multi-Modal Ensemble Classifier for Protein Secondary Structure Prediction.- Breast Cancer Brain Metastasis: Automated MRI Image Analysis for the Prediction of Primary Cancer Using Radiomics.- An Adaptive Semi-Automated Integrated System for Multiple Sclerosis Lesion Segmentation in Longitudinal MRI Scans Based on a Convolutional Neural Network.- A Three-Dimensional Reconstruction Integrated System for Brain Multiple Sclerosis Lesions.- Rule Extraction in the Assessment of Brain MRI Lesions in Multiple Sclerosis: Preliminary Findings.- Invariant Moments, Textural and Deep features for Diagnostic MR and CT Image Retrieval.- Toward multiwavelet Haar-Schauder entropy for biomedical signal reconstruction.- Machine Learning.- Handling Missing Observations with an RNN-based Prediction-Update Cycle.- eGAN: Unsupervised approach to class imbalance using transfer learning.- Progressive Contextual Excitation for Smart Farming Application.- Fine-Grained Image Classification for Pollen Grain Microscope Images.- Adaptive Style Transfer Using SISR.- Object-Centric Anomaly Detection using Memory Augmentation.- Document Language Classification: Hierarchical Model With Deep Learning Approach.- Parsing Digitized Vietnamese Paper Documents.- EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task.- When Deep Learners Change Their Mind: Learning Dynamics for Active Learning.- Learning to Navigate in the Gaussian Mixture Surface.- A Deep Hybrid Approach For Hate Speech Analysis.- On improving generalization of CNN-based image classification with delineation maps using the CORF push-pull inhibition operator.- Fast Hand Detection in Collaborative Learning Environments.- Assessing the Role of Boundary-level Objectives in Indoor Semantic Segmentation.- Skin lesion classification using convolutional neural networks based on Multi-Features Extraction.- Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing.- Layer-wise Relevance Propagation based Sample Condensation for Kernel Machines.-
£62.99
Springer Nature Switzerland AG Misinformation and Disinformation: Detecting
Book SynopsisThis book, geared towards both students and professionals, examines the synthesis of artificial intelligence (AI) and psychology in detecting mis-/disinformation in digital media content, and suggests practical means to intervene and curtail this current global ‘infodemic’. This interdisciplinary book explores technological, psychological, philosophical, and linguistic insights into the nature of truth and deception, trust and credibility, cognitive biases and logical fallacies and how, through AI and human intervention, content users can be alerted to the presence of deception. The author investigates how AI can mimic the procedures and know-hows of humans, showing how AI can help spot fakes and how AI tools can work to debunk rumors and fact-check. The book describes how AI detection systems work and how they fit with broader societal and individual concerns. Each chapter focuses attention on key concepts and their inter-connection. The first part of the book seeks theoretical footing to understand our interactions with new information and reviews relevant empirical findings in behavioral sciences. The second part is about applied knowledge. The author looks at several known practices that guard us against deception, and provides several real-world examples of manipulative persuasive techniques in advertising, political propaganda, and public relations. She provides links to the downloadable executable files to three AI applications (clickbait, satire, and falsehood detectors) via LiT.RL GitHub, an open access repository. The book is useful to students and professionals studying AI and media studies as well as library and information professionals. Examines how artificial intelligence (AI) and psychology can aid in detecting mis-/disinformation and the language of deceit in digital media content; Suggests practical computational means to intervene and curtail the global ‘infodemic’ of fake news; Presents how AI can sift, sort, and shuffle digital content, to reduce the amount of content needed to be reviewed by humans. Table of ContentsIntroduction.- Infodemic in the Digital Media Content.- Part I. Human Nature of Deception and Perceptions of Truth.- Psychology of Mis-/Disinformation and Language of Deceit.- Library and Information Science on Credibility and Trust in Computing.- Philosophies of Truth.- Part II. Applied Practices and Artificial Intelligence (AI).- Investigation in Law Enforcement, Journalism, and Sciences.- Manipulation in Marketing, Advertising, and Public Relations.- Artificially Intelligent Solutions: Detection, Debunking, Fact-Checking.- Lessons for Infodemic Control and Future of Digital Content Verification.- Conclusion.
£40.49
Springer International Publishing AG Computational Neuroscience: Third Latin American Workshop, LAWCN 2021, São Luís, Brazil, December 8–10, 2021, Revised Selected Papers
Book SynopsisThis book constitutes the refereed proceedings of the Third Latin American Workshop, LAWCN 2021, held in Sao Luis do Maranhao, Brazil, during December 8–10, 2021.The 13 full papers and 3 short papers included in this book were carefully reviewed and selected from 27 submissions. They were organized in topical sections as follows: Interdisciplinary applications of Artificial Intelligence (AI) and Machine Learning (ML); AI and ML applied to robotics; AI and ML applied to biomedical sciences; Health issues and computational neuroscience; Software and hardware implementations in neuroscience; and Neuroengineering – science and technology.Table of ContentsInterdisciplinary applications of Artificial Intelligence (AI) and Machine Learning (ML).- Semantic segmentation of the cultivated area of plantations with U-net.- Use and Interpretation of Item Response Theory applied to Machine Learning.- AI and ML applied to robotics.- Towards Loop Closure Detection for SLAM Applications using Bag of Visual Features: Experiments and Simulation.- Loss Function Regularisation on the Iterated Racing Procedure for Automatic Tuning of RatSLAM Parameters.- Controlling the UR3 robotic arm using a leap motion: a comparative study.- AI and ML applied to biomedical sciences.- Web service based epileptic seizure detection by applying machine learning techniques.- Health issues and computational neuroscience.- Machine Learning Search of Novel Selective NaV1.2 and NaV1.6 Inhibitors as Potential Treatment against Dravet Syndrome.- Implementation of intra and extracellular nonperiodic scale-free stimulation in silico for the NEURON simulator.- In silico investigation of the effects of distinct temporal patterns of electrical stimulation to the amygdala using a network of Izhikevich neurons.- Software and hardware implementations in neuroscience.- Brain connectivity measures in EEG-based biometry for epilepsy patients.- A multiplatform output stage for the development of current-fixed electrical stimulators applied to neural electrophysiology.- Neuroengineering – science and technology.- Physiological self-regulation using biofeedback training: from concept to clinical applicability.- Movement-Related Electroencephalography in Stroke Patients across a Brain-Computer Interface-based Intervention.- Electrophysiological Correlates of Freeze of Gait in Parkinson Disease: Increased STN-LFP Alpha Power and the Possible Role of Attentional Circuits.- Effect of hand dominance when decoding motor imagery grasping tasks within the same hand.- Kinematic responses as a control strategy to visual occlusion.
£58.49
Springer International Publishing AG High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production: 12th International Conference, HPCST 2022, Barnaul, Russia, May 20–21, 2022, Revised Selected Papers
Book SynopsisThis book constitutes the refereed proceedings of the 12th International Conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production, HPCST 2022, held in Barnaul, Russia, during May 20–21, 2022. The 23 full papers included in this book were carefully reviewed and selected from 116 submissions. They were organized in topical sections as follows: hardware for high-performance computing and signal processing; information technologies and computer simulation of physical phenomena; computing technologies in data analysis and decision making; and computing technologies in information security applications.Table of ContentsHardware for High-Performance Computing and Signal Processing.- Hierarchical Encoder-Decoder Neural Network with Self-Attention for Single-Channel Speech Denoising.- Calculation of Activation Functions in FPGA-Based Neuroprocessors Using the Cordic Algorithm.- Frequency-Domain Generalized Phase Transform Method in Pipeline Leaks Locating.- Information Technologies and Computer Simulation of Physical Phenomena.- Inverse Problem Regularization of Parametric Identification of the Particle Temperature Distribution of Gas-Thermal Flow with Optimization of Its Solution.- Application of the Heterogeneous Multiscale Finite Element Method for Modelling the Compressibility of Porous Media.- Parallel Non-Conforming Finite Element Technique for Mathematical Simulation of Fluid Flow in Multiscale Porous Media.- Darcy Problem Solution in a Mixed Formulation for Geological Media of Various Structures.- Mathematical Modeling of the Electromagnetic Field from a Solenoidal Coil in the Frequency Domain.- Computing Technologies in Data Analysis and Decision Making.- Novel Planning Technique for ERP Systems Implementation Projects.- Development of an Automated Document Classification System with a Predefined Structure.- Ontology-Based Data Mining Platform for Diagnosing Sowing Quality of Wheat Seeds.- Algorithm for the Classification of Coronary Heart Disease Based on the Use of Symptom Complexes in the Cardiovascular Environment.- Business Process Designing of the Institutional Ranking System of Higher Education of the Kyrgyz Republic.- Evaluation of the Efficiency of the Vaccination against Chickenpox Based on the Results of Simulation Modeling.- Hierarchical Volume Mesh Model of Heterogeneous Media Based on Non-Destructive Imaging Data.- Modeling а Potential Plant Habitat by Ensemble Machine Learning.- Information and Computing Technologies in Automation and Control Science.- Adaptive Traffic Signal Control Based on a Macroscopic Model of the Transport Network.- Optimization and Robustization of Angular Motion Control of an Automatic Maneuverable Aerial Vehicle.- Synthesis of the Rational Analyzing Function for Feature Extraction of Signals from the Electrostatic Location System.- Application of Computer Numerical Control in Eddy-Current Study Methods.- Computing Technologies in Information Security Applications.- Open-Source Solution for Identification and Blocking of Anomalous BGP-4 Routing Information.- Simulation of DDoS Attacks on LTE and LoRaWAN Protocols in the NS-3 Network Simulator.- Transaction-Oriented Approach to the Design of Information Systems Based on Formal Grammar.
£58.49
Springer International Publishing AG Machine Learning, Image Processing, Network Security and Data Sciences: 4th International Conference, MIND 2022, Virtual Event, January 19–20, 2023, Proceedings, Part II
Book SynopsisThis two-volume set (CCIS 1762-1763) constitutes the refereed proceedings of the 4th International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2022, held in Bhopal, India, in December 2022. The 64 papers presented in this two-volume set were thoroughly reviewed and selected from 399 submissions. The papers are organized according to the following topical sections: machine learning and computational intelligence; data sciences; image processing and computer vision; network and cyber security.Table of ContentsMachine Learning and Computational Intelligence.- Data Sciences.- Image Processing and Computer Vision.- Network and Cyber Security.
£58.49
Springer International Publishing AG Computer Vision, Imaging and Computer Graphics Theory and Applications: 16th International Joint Conference, VISIGRAPP 2021, Virtual Event, February 8–10, 2021, Revised Selected Papers
Book SynopsisThis book constitutes the refereed proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021, held as a virtual event, February 8–10, 2021.The 16 full papers presented in this volume were carefully reviewed and selected from 371 submissions. The purpose of VISIGRAPP is to bring together researchers and practitioners interested in both theoretical advances and applications of computer vision, computer graphics and information visualization. VISIGRAPP is composed of four co-located conferences, each specialized in at least one of the aforementioned main knowledge areas, namely GRAPP, IVAPP, HUCAPP and VISAPP. The contributions were organized in topical sections as follows: Computer Graphics Theory and Applications; Human Computer Interaction Theory and Applications; Information Visualization Theory and Applications; Computer Vision Theory and Applications.Table of ContentsComputer Graphics Theory and Applications.- Impact of Avatar Representation in a Virtual Reality-based Multi-user Tunnel Fire Simulator for Training Purposes.- Facade Layout Completion with Long Short-term Memory Networks.- Human Computer Interaction Theory and Applications.- Generating Haptic Sensations over Spherical Surface.- Effects of Emotion-Induction Words on Memory and Pupillary Reactions while Viewing Visual Stimuli with Audio Guide.- A Bimanual Flick-based Japanese Software Keyboard using Direct Kanji Input.- Comparison of Cardiac Activity and Subjective Measures during Virtual Reality and Real Aircraft Flight.- Information Visualization Theory and Applications.- Improving Self-supervised Dimensionality Reduction: Exploring Hyperparameters and Pseudo-labeling Strategies.- Visualization of Source Code Similarity using 2.5D Semantic Software Maps.- Revisiting Order-Preserving, Gap-Avoiding Rectangle Packing.- Exploratory Data Analysis of Population Level Smartphone-sensed Data.- Towards Interactive Geovisualization Authoring Toolkit for Industry Use Cases.- Computer Vision Theory and Applications.- Global-fiirst Training Strategy with Convolutional Neural Networks to Improve Scale Invariance.- Spline-based Dense Medial Descriptors for Image Simplification using Saliency Maps.- BS-GAENets: Brain-spatial Feature Learning Via a Graph Deep Autoencoder for Multi-modal Neuroimaging Analysis.- Enhancing Backlight and Spotlight Images by the Retinex-inspired Bilateral Filter SuPeR-B.- Rethinking RNN-based Video Object Segmentation.
£58.49
Springer International Publishing AG Computer Vision – ACCV 2022 Workshops: 16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022, Revised Selected Papers
Book SynopsisThis book constitutes the refereed post-conference proceedings of the workshops held at the 16th Asian Conference on Computer Vision, ACCV 2022, which took place in Macao, China, in December 2022. The 25 papers included in this book were carefully reviewed and selected from 40 submissions. They have been organized in topical sections as follows: Learning with limited data for face analysis; adversarial machine learning towards advanced vision systems; computer vision for medical computing; machine learning and computing for visual semantic analysis; vision transformers theory and applications; and deep learning-based small object detection from images and videos.Table of ContentsLearning with Limited Data for Face Analysis.- FAPN: Face Alignment Propagation Network for Face Video Super-Resolution.- Micro-expression recognition using a shallow ConvLSTM-based network.- Adversarial Machine Learning towards Advanced Vision Systems.- ADVFilter: Adversarial Example Generated by Perturbing Optical Path.- Enhancing Federated Learning Robustness Through clustering Non-IID Features.- Towards Improving the Anti-attack Capability of the RangeNet++.- Computer Vision for Medical Computing.- Ensemble Model of Visual Transformer and CNN Helps BA Diagnosis for Doctors in Underdeveloped Areas.- Understanding Tumor Micro Environment using Graph theory.- Handling Domain Shift for Lesion Detection via Semi-Supervised Domain Adaptation.- Photorealistic Facial Wrinkles Removal.- Improving Segmentation of Breast Arterial Calcifications from Digital Mammography: Good Annotation Is All You Need.- Machine Learning and Computing for Visual Semantic Analysis.- Towards Scene Understanding for Autonomous Operations on Airport Aprons.- Lightweight Hyperspectral Image Reconstruction Network with Deep Feature Hallucination.- A Transformer-based Model for Preoperative Early Recurrence Prediction of Hepatocellular Carcinoma with Muti-p.- CaltechFN: Distorted and Partially Occluded Digits.- Temporal Extension Topology Learning for Video-based Person Re-Identification.- Deep RGB-driven Learning Network for Unsupervised Hyperspectral Image Super-resolution.- Gift from nature: Potential Energy Minimization for explainable dataset distillation.- Object Centric Point Sets Feature Learning with Matrix Decomposition.- Aerial Image Segmentation via Noise Dispelling and Content Distilling.- Vision Transformers Theory and Applications.- Temporal Cross-attention for Action Recognition.- Transformer Based Motion In-Betweening.- Convolutional point Transformer.- Cross-Attention Transformer for Video Interpolation.- Deep Learning-Based Small Object Detection from Images and Videos.- Evaluating and Bench-marking Object Detection Models for Traffic Sign and Traffic Light Datasets.- Exploring Spatial-temporal Instance Relationships In an Intermediate Domain For Image-to-video Object Detection.
£56.99
De Gruyter Computational Intelligence in Software Modeling
Book SynopsisResearchers, academicians and professionals expone in this book their research in the application of intelligent computing techniques to software engineering. As software systems are becoming larger and complex, software engineering tasks become increasingly costly and prone to errors. Evolutionary algorithms, machine learning approaches, meta-heuristic algorithms, and others techniques can help the effi ciency of software engineering.
£101.25
Springer International Publishing AG Image Analysis: 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway, June 12–14, 2017, Proceedings, Part I
Book SynopsisThe two-volume set LNCS 10269 and 10270 constitutes the refereed proceedings of the 20th Scandinavian Conference on Image Analysis, SCIA 2017, held in Tromsø, Norway, in June 2017. The 87 revised papers presented were carefully reviewed and selected from 133 submissions. The contributions are structured in topical sections on history of SCIA; motion analysis and 3D vision; pattern detection and recognition; machine learning; image processing and applications; feature extraction and segmentation; remote sensing; medical and biomedical image analysis; faces, gestures and multispectral analysis.Table of ContentsHistory of SCIA.- Motion analysis and 3D vision.- Pattern detection and recognition.- Machine learning.- Image processing and applications.- Feature extraction and segmentation.- Remote sensing.- Medical and biomedical image analysis.- Faces, gestures and multispectral analysis.
£62.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Image Processing Based on Partial Differential Equations: Proceedings of the International Conference on PDE-Based Image Processing and Related Inverse Problems, CMA, Oslo, August 8-12, 2005
Book SynopsisThis book publishes a collection of original scientific research articles that address the state-of-art in using partial differential equations for image and signal processing. Coverage includes: level set methods for image segmentation and construction, denoising techniques, digital image inpainting, image dejittering, image registration, and fast numerical algorithms for solving these problems. Table of ContentsDigital Image Inpainting, Image Dejittering, and Optical Flow Estimation.- Image Inpainting Using a TV-Stokes Equation.- Error Analysis for H1 Based Wavelet Interpolations.- Image Dejittering Based on Slicing Moments.- CLG Method for Optical Flow Estimation Based on Gradient Constancy Assumption.- Denoising and Total Variation Methods.- On Multigrids for Solving a Class of Improved Total Variation Based Staircasing Reduction Models.- A Method for Total Variation-based Reconstruction of Noisy and Blurred Images.- Minimization of an Edge-Preserving Regularization Functional by Conjugate Gradient Type Methods.- A Newton-type Total Variation Diminishing Flow.- Chromaticity Denoising using Solution to the Skorokhod Problem.- Improved 3D Reconstruction of Interphase Chromosomes Based on Nonlinear Diffusion Filtering.- Image Segmentation.- Some Recent Developments in Variational Image Segmentation.- Application of Non-Convex BV Regularization for Image Segmentation.- Region-Based Variational Problems and Normal Alignment – Geometric Interpretation of Descent PDEs.- Fast PCLSM with Newton Updating Algorithm.- Fast Numerical Methods.- Nonlinear Multilevel Schemes for Solving the Total Variation Image Minimization Problem.- Fast Implementation of Piecewise Constant Level Set Methods.- The Multigrid Image Transform.- Minimally Stochastic Schemes for Singular Diffusion Equations.- Image Registration.- Total Variation Based Image Registration.- Variational Image Registration Allowing for Discontinuities in the Displacement Field.- Inverse Problems.- Shape Reconstruction from Two-Phase Incompressible Flow Data using Level Sets.- Reservoir Description Using a Binary Level Set Approach with Additional Prior Information About the Reservoir Model.
£161.99
John Wiley & Sons Inc An Introduction to 3D Computer Vision Techniques
Book SynopsisComputer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene.Trade Review“This text is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.” (Zentralblatt MATH, 2012) Table of ContentsPreface xv Acknowledgements xvii Notation and Abbreviations xix Part I 1 1 Introduction 3 1.1 Stereo-pair Images and Depth Perception 4 1.2 3D Vision Systems 4 1.3 3D Vision Applications 5 1.4 Contents Overview: The 3D Vision Task in Stages 6 2 Brief History of Research on Vision 9 2.1 Abstract 9 2.2 Retrospective of Vision Research 9 2.3 Closure 14 2.3.1 Further Reading 14 Part II 15 3 2D and 3D Vision Formation 17 3.1 Abstract 17 3.2 Human Visual System 18 3.3 Geometry and Acquisition of a Single Image 23 3.3.1 Projective Transformation 24 3.3.2 Simple Camera System: the Pin-hole Model 24 3.3.3 Projective Transformation of the Pin-hole Camera 28 3.3.4 Special Camera Setups 29 3.3.5 Parameters of Real Camera Systems 30 3.4 Stereoscopic Acquisition Systems 31 3.4.1 Epipolar Geometry 31 3.4.2 Canonical Stereoscopic System 36 3.4.3 Disparity in the General Case 38 3.4.4 Bifocal, Trifocal and Multifocal Tensors 39 3.4.5 Finding the Essential and Fundamental Matrices 41 3.4.6 Dealing with Outliers 49 3.4.7 Catadioptric Stereo Systems 54 3.4.8 Image Rectification 55 3.4.9 Depth Resolution in Stereo Setups 59 3.4.10 Stereo Images and Reference Data 61 3.5 Stereo Matching Constraints 66 3.6 Calibration of Cameras 70 3.6.1 Standard Calibration Methods 71 3.6.2 Photometric Calibration 73 3.6.3 Self-calibration 73 3.6.4 Calibration of the Stereo Setup 74 3.7 Practical Examples 75 3.7.1 Image Representation and Basic Structures 75 3.8 Appendix: Derivation of the Pin-hole Camera Transformation 91 3.9 Closure 93 3.9.1 Further Reading 93 3.9.2 Problems and Exercises 94 4 Low-level Image Processing for Image Matching 95 4.1 Abstract 95 4.2 Basic Concepts 95 4.2.1 Convolution and Filtering 95 4.2.2 Filter Separability 97 4.3 Discrete Averaging 99 4.3.1 Gaussian Filter 100 4.3.2 Binomial Filter 101 4.4 Discrete Differentiation 105 4.4.1 Optimized Differentiating Filters 105 4.4.2 Savitzky–Golay Filters 108 4.5 Edge Detection 115 4.5.1 Edges from Signal Gradient 117 4.5.2 Edges from the Savitzky–Golay Filter 119 4.5.3 Laplacian of Gaussian 120 4.5.4 Difference of Gaussians 126 4.5.5 Morphological Edge Detector 127 4.6 Structural Tensor 127 4.6.1 Locally Oriented Neighbourhoods in Images 128 4.6.2 Tensor Representation of Local Neighbourhoods 133 4.6.3 Multichannel Image Processing with Structural Tensor 143 4.7 Corner Detection 144 4.7.1 The Most Common Corner Detectors 144 4.7.2 Corner Detection with the Structural Tensor 149 4.8 Practical Examples 151 4.8.1 C++ Implementations 151 4.8.2 Implementation of the Morphological Operators 157 4.8.3 Examples in Matlab: Computation of the SVD 161 4.9 Closure 162 4.9.1 Further Reading 163 4.9.2 Problems and Exercises 163 5 Scale-space Vision 165 5.1 Abstract 165 5.2 Basic Concepts 165 5.2.1 Context 165 5.2.2 Image Scale 166 5.2.3 Image Matching Over Scale 166 5.3 Constructing a Scale-space 168 5.3.1 Gaussian Scale-space 168 5.3.2 Differential Scale-space 170 5.4 Multi-resolution Pyramids 172 5.4.1 Introducing Multi-resolution Pyramids 172 5.4.2 How to Build Pyramids 175 5.4.3 Constructing Regular Gaussian Pyramids 175 5.4.4 Laplacian of Gaussian Pyramids 177 5.4.5 Expanding Pyramid Levels 178 5.4.6 Semi-pyramids 179 5.5 Practical Examples 181 5.5.1 C++ Examples 181 5.5.2 Matlab Examples 186 5.6 Closure 191 5.6.1 Chapter Summary 191 5.6.2 Further Reading 191 5.6.3 Problems and Exercises 192 6 Image Matching Algorithms 193 6.1 Abstract 193 6.2 Basic Concepts 193 6.3 Match Measures 194 6.3.1 Distances of Image Regions 194 6.3.2 Matching Distances for Bit Strings 198 6.3.3 Matching Distances for Multichannel Images 199 6.3.4 Measures Based on Theory of Information 202 6.3.5 Histogram Matching 205 6.3.6 Efficient Computations of Distances 206 6.3.7 Nonparametric Image Transformations 209 6.3.8 Log-polar Transformation for Image Matching 218 6.4 Computational Aspects of Matching 222 6.4.1 Occlusions 222 6.4.2 Disparity Estimation with Subpixel Accuracy 224 6.4.3 Evaluation Methods for Stereo Algorithms 226 6.5 Diversity of Stereo Matching Methods 229 6.5.1 Structure of Stereo Matching Algorithms 233 6.6 Area-based Matching 238 6.6.1 Basic Search Approach 239 6.6.2 Interpreting Match Cost 241 6.6.3 Point-oriented Implementation 245 6.6.4 Disparity-oriented Implementation 250 6.6.5 Complexity of Area-based Matching 256 6.6.6 Disparity Map Cross-checking 257 6.6.7 Area-based Matching in Practice 259 6.7 Area-based Elastic Matching 273 6.7.1 Elastic Matching at a Single Scale 273 6.7.2 Elastic Matching Concept 278 6.7.3 Scale-based Search 280 6.7.4 Coarse-to-fine Matching Over Scale 283 6.7.5 Scale Subdivision 284 6.7.6 Confidence Over Scale 285 6.7.7 Final Multi-resolution Matcher 286 6.8 Feature-based Image Matching 288 6.8.1 Zero-crossing Matching 289 6.8.2 Corner-based Matching 292 6.8.3 Edge-based Matching: The Shirai Method 295 6.9 Gradient-based Matching 296 6.10 Method of Dynamic Programming 298 6.10.1 Dynamic Programming Formulation of the Stereo Problem 301 6.11 Graph Cut Approach 306 6.11.1 Graph Cut Algorithm 306 6.11.2 Stereo as a Voxel Labelling Problem 311 6.11.3 Stereo as a Pixel Labelling Problem 312 6.12 Optical Flow 314 6.13 Practical Examples 318 6.13.1 Stereo Matching Hierarchy in C++ 318 6.13.2 Log-polar Transformation 319 6.14 Closure 321 6.14.1 Further Reading 321 6.14.2 Problems and Exercises 322 7 Space Reconstruction and Multiview Integration 323 7.1 Abstract 323 7.2 General 3D Reconstruction 323 7.2.1 Triangulation 324 7.2.2 Reconstruction up to a Scale 325 7.2.3 Reconstruction up to a Projective Transformation 327 7.3 Multiview Integration 329 7.3.1 Implicit Surfaces and Marching Cubes 330 7.3.2 Direct Mesh Integration 338 7.4 Closure 342 7.4.1 Further Reading 342 8 Case Examples 343 8.1 Abstract 343 8.2 3D System for Vision-Impaired Persons 343 8.3 Face and Body Modelling 345 8.3.1 Development of Face and Body Capture Systems 345 8.3.2 Imaging Resolution, 3D Resolution and Implications for Applications 346 8.3.3 3D Capture and Analysis Pipeline for Constructing Virtual Humans 350 8.4 Clinical and Veterinary Applications 352 8.4.1 Development of 3D Clinical Photography 352 8.4.2 Clinical Requirements for 3D Imaging 353 8.4.3 Clinical Assessment Based on 3D Surface Anatomy 353 8.4.4 Extraction of Basic 3D Anatomic Measurements 354 8.4.5 Vector Field Surface Analysis by Means of Dense Correspondences 357 8.4.6 Eigenspace Methods 359 8.4.7 Clinical and Veterinary Examples 362 8.4.8 Multimodal 3D Imaging 367 8.5 Movie Restoration 370 8.6 Closure 374 8.6.1 Further Reading 374 Part III 375 9 Basics of the Projective Geometry 377 9.1 Abstract 377 9.2 Homogeneous Coordinates 377 9.3 Point, Line and the Rule of Duality 379 9.4 Point and Line at Infinity 380 9.5 Basics on Conics 382 9.5.1 Conics in ℘2 382 9.5.2 Conics in ℘2 384 9.6 Group of Projective Transformations 385 9.6.1 Projective Base 385 9.6.2 Hyperplanes 386 9.6.3 Projective Homographies 386 9.7 Projective Invariants 387 9.8 Closure 388 9.8.1 Further Reading 389 10 Basics of Tensor Calculus for Image Processing 391 10.1 Abstract 391 10.2 Basic Concepts 391 10.2.1 Linear Operators 392 10.2.2 Change of Coordinate Systems: Jacobians 393 10.3 Change of a Base 394 10.4 Laws of Tensor Transformations 396 10.5 The Metric Tensor 397 10.5.1 Covariant and Contravariant Components in a Curvilinear Coordinate System 397 10.5.2 The First Fundamental Form 399 10.6 Simple Tensor Algebra 399 10.6.1 Tensor Summation 399 10.6.2 Tensor Product 400 10.6.3 Contraction and Tensor Inner Product 400 10.6.4 Reduction to Principal Axes 400 10.6.5 Tensor Invariants 401 10.7 Closure 401 10.7.1 Further Reading 401 11 Distortions and Noise in Images 403 11.1 Abstract 403 11.2 Types and Models of Noise 403 11.3 Generating Noisy Test Images 405 11.4 Generating Random Numbers with Normal Distributions 407 11.5 Closure 408 11.5.1 Further Reading 408 12 Image Warping Procedures 409 12.1 Abstract 409 12.2 Architecture of the Warping System 409 12.3 Coordinate Transformation Module 410 12.3.1 Projective and Affine Transformations of a Plane 410 12.3.2 Polynomial Transformations 411 12.3.3 Generic Coordinates Mapping 412 12.4 Interpolation of Pixel Values 412 12.4.1 Bilinear Interpolation 412 12.4.2 Interpolation of Nonscalar-Valued Pixels 414 12.5 The Warp Engine 414 12.6 Software Model of the Warping Schemes 415 12.6.1 Coordinate Transformation Hierarchy 415 12.6.2 Interpolation Hierarchy 416 12.6.3 Image Warp Hierarchy 416 12.7 Warp Examples 419 12.8 Finding the Linear Transformation from Point Correspondences 420 12.8.1 Linear Algebra on Images 424 12.9 Closure 427 12.9.1 Further Reading 428 13 Programming Techniques for Image Processing and Computer Vision 429 13.1 Abstract 429 13.2 Useful Techniques and Methodology 430 13.2.1 Design and Implementation 430 13.2.2 Template Classes 436 13.2.3 Asserting Code Correctness 438 13.2.4 Debugging Issues 440 13.3 Design Patterns 441 13.3.1 Template Function Objects 441 13.3.2 Handle-body or Bridge 442 13.3.3 Composite 445 13.3.4 Strategy 447 13.3.5 Class Policies and Traits 448 13.3.6 Singleton 450 13.3.7 Proxy 450 13.3.8 Factory Method 451 13.3.9 Prototype 452 13.4 Object Lifetime and Memory Management 453 13.5 Image Processing Platforms 455 13.5.1 Image Processing Libraries 455 13.5.2 Writing Software for Different Platforms 455 13.6 Closure 456 13.6.1 Further Reading 456 14 Image Processing Library 457 References 459 Index 475
£99.86
John Wiley & Sons Inc Image Processing 27 Adaptive and Cognitive
Book SynopsisIntelligent Image Processing describes the EyeTap technology that allows non-invasive tapping into the human eye through devices built into eyeglass frames. This isn't merely about a computer screen inside eyeglasses, but rather the ability to have a shared telepathic experience among viewers.Table of ContentsPreface 1 Humanistic Intelligence as a Basis for Intelligent Image Processing 1.1 Humanistic Intelligence/ 1.2 "WearComp" as Means of Realizing Humanistic Intelligence 1.3 Practical Embodiments of Humanistic Intelligence 2 Where on the Body is the Best Place for a Personal Imaging System? 2.1 Portable Imaging Systems 2.2 Personal Handheld Systems 2.3 Concomitant Cover Activities and the Videoclips Camera System 2.4 The Wristwatch Videophone: A Fully Functional "Always Ready" Prototype 2.5 Telepointer: Wearable Hands-Free Completely Self-Contained Visual Augmented Reality 2.6 Portable Personal Pulse Doppler Radar Vision System Based on Time-Frequency Analysis and q-Chirplet Transform 2.7 When Both Camera and Display are Headworn: Personal Imaging and Mediated Reality 2.8 Partially Mediated Reality 2.9 Seeing "Eye-to-Eye" 2.10 Exercises, Problem Sets, and Homework 3 The EyeTap Principle: Effectively Locating the Camera Inside the Eye as an Alternative to Wearable Camera Systems 3.1 A Personal Imaging System for Lifelong Video Capture 3.2 The EyeTap Principle 3.3 Practical Embodiments of EyeTap 3.4 Problems with Previously Known Camera Viewfinders 3.5 The Aremac 3.6 The Foveated Personal Imaging System 3.7 Teaching the EyeTap Principle 3.8 Calibration of EyeTap Systems 3.9 Using the Device as a Reality Mediator 3.10 User Studies 3.11 Summary and Conclusions 3.12 Exercises, Problem Sets, and Homework 4 Comparametric Equations, Quantigraphic Image Processing, and Comparagraphic Rendering 4.1 Historical Background 4.2 The Wyckoff Principle and the Range of Light 4.3 Comparametric Image Processing: Comparing Differently Exposed Images of the Same Subject Matter 4.4 The Comparagram: Practical Implementations of Comparanalysis 4.5 Spatiotonal Photoquantigraphic Filters 4.6 Glossary of Functions 4.7 Exercises, Problem Sets, and Homework 5 Lightspace and Antihomomorphic Vector Spaces 5.1 Lightspace 5.2 The Lightspace Analysis Function 5.3 The "Spotflash" Primitive 5.4 LAF×LSF Imaging ("Lightspace") 5.5 Lightspace Subspaces 5.6 "Lightvector" Subspace 5.7 Painting with Lightvectors: Photographic/Videographic Origins and Applications of WearComp-Based Mediated Reality 5.8 Collaborative Mediated Reality Field Trials 5.9 Conclusions 5.10 Exercises, Problem Sets, and Homework 6 VideoOrbits: The Projective Geometry Renaissance 6.1 VideoOrbits 6.2 Background 6.3 Framework: Motion Parameter Estimation and Optical Flow 6.4 Multiscale Implementations in 2-D 6.5 Performance and Applications 6.6 AGC and the Range of Light 6.7 Joint Estimation of Both Domain and Range Coordinate Transformations 6.8 The Big Picture 6.9 Reality Window Manager 6.10 Application of Orbits: The Photonic Firewall 6.11 All the World's a Skinner Box 6.12 Blocking Spam with a Photonic Filter 6.13 Exercises, Problem Sets, and Homework Appendix A: Safety First! Appendix B: Multiambic Keyer for Use While Engaged in Other Activities B.1 Introduction B.2 Background and Terminology on Keyers B.3 Optimal Keyer Design: The Conformal Keyer B.4 The Seven Stages of a Keypress B.5 The Pentakeyer B.6 Redundancy B.7 Ordinally Conditional Modifiers B.8 Rollover B.8.1 Example of Rollover on a Cybernetic Keyer B.9 Further Increasing the Chordic Redundancy Factor: A More Expressive Keyer B.10 Including One Time Constant B.11 Making a Conformal Multiambic Keyer B.12 Comparison to Related Work B.13 Conclusion B.14 Acknowledgments Appendix C: WearCam GNUX Howto C.1 Installing GNUX on WearComps C.2 Getting Started C.3 Stop the Virus from Running C.4 Making Room for an Operating System C.5 Other Needed Files C.6 Defrag / 323 C.7 Fips C.8 Starting Up in GNUX with Ramdisk Appendix D: How to Build a Covert Computer Imaging System into Ordinary Looking Sunglasses D.1 The Move from Sixth-Generation WearComp to Seventh-Generation D.2 Label the Wires! D.3 Soldering Wires Directly to the Kopin CyberDisplay D.4 Completing the Computershades Bibliography Index
£127.76
John Wiley and Sons Ltd Understanding Vision
Book SynopsisIn recent years there have been major advances in understanding visual processing. This work brings together experts from various disciplines, ranging from computer science to neuropsychology, to discuss how the work carried out in their field fits into the broader context of vision research.Table of ContentsContructing the perception of surfaces from multiple cues, Kent A. Stevens' visual analysis and representation of spatial relations, Roger J. Watt; modern theories of Gestalt perception, Stephen J. Palmer; thinking visually, Kris N. Kirby and Stephen M. Kosslyn; perceiving and recognizing faces, Vicki Bruce; the breakdown approach to visual perception - neuropsychological studies of object recognition, Glyn W. Humphreys et al; mechanisms which mediate discrimination of 2-D spatial patterns in distributed images, Keith H. Ruddock; the analysis of 3-D shape - psychological principles and neural mechanisms, Andrew J. Parker et al; identification of disoriented objects - a dual-systems theory, Pierre Jolicoeur; surface layout from retinal flow, Mike Harris et al; neural facades - visual representations of static and moving form-and-colour-and-depth, Stephen Grossberg.
£37.00
John Wiley & Sons Inc A Practical Introduction to Computer Vision with
Book SynopsisExplains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries Computer Vision is a rapidly expanding area and it is becoming progressively easier for developers to make use of this field due to the ready availability of high quality libraries (such as OpenCV 2). This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision. The book will explain how to use the relevant OpenCV library routines and will be accompanied by a full working program including the code snippets from the text. This textbook is a heavily illustrated, practical introduction to an exciting field, the applications of which are becoming almost ubiquitous. We are now surrounded by cameras, for example cameras on computers & tablets/ cameras built into our mobile phones/ cameras in games Trade Review“Although there are many computer vision books on the market that offer a more comprehensive approach to explaining the computer vision concepts, extremely few offer such comprehensive practical examples. In this context, the book would be very welcome by beginner code developers." (Computing Reviews, 8 August 2014) Table of ContentsPreface xiii 1 Introduction 1 1.1 A Difficult Problem 1 1.2 The Human Vision System 2 1.3 Practical Applications of Computer Vision 3 1.4 The Future of Computer Vision 5 1.5 Material in This Textbook 6 1.6 Going Further with Computer Vision 7 2 Images 9 2.1 Cameras 9 2.1.1 The Simple Pinhole Camera Model 9 2.2 Images 10 2.2.1 Sampling 11 2.2.2 Quantisation 11 2.3 Colour Images 13 2.3.1 Red–Green–Blue (RGB) Images 14 2.3.2 Cyan–Magenta–Yellow (CMY) Images 17 2.3.3 YUV Images 17 2.3.4 Hue Luminance Saturation (HLS) Images 18 2.3.5 Other Colour Spaces 20 2.3.6 Some Colour Applications 20 2.4 Noise 22 2.4.1 Types of Noise 23 2.4.2 Noise Models 25 2.4.3 Noise Generation 26 2.4.4 Noise Evaluation 26 2.5 Smoothing 27 2.5.1 Image Averaging 27 2.5.2 Local Averaging and Gaussian Smoothing 28 2.5.3 Rotating Mask 30 2.5.4 Median Filter 31 3 Histograms 35 3.1 1D Histograms 35 3.1.1 Histogram Smoothing 36 3.1.2 Colour Histograms 37 3.2 3D Histograms 39 3.3 Histogram/Image Equalisation 40 3.4 Histogram Comparison 41 3.5 Back-projection 43 3.6 k-means Clustering 44 4 Binary Vision 49 4.1 Thresholding 49 4.1.1 Thresholding Problems 50 4.2 Threshold Detection Methods 51 4.2.1 Bimodal Histogram Analysis 52 4.2.2 Optimal Thresholding 52 4.2.3 Otsu Thresholding 54 4.3 Variations on Thresholding 56 4.3.1 Adaptive Thresholding 56 4.3.2 Band Thresholding 57 4.3.3 Semi-thresholding 58 4.3.4 Multispectral Thresholding 58 4.4 Mathematical Morphology 59 4.4.1 Dilation 60 4.4.2 Erosion 62 4.4.3 Opening and Closing 63 4.4.4 Grey-scale and Colour Morphology 65 4.5 Connectivity 66 4.5.1 Connectedness: Paradoxes and Solutions 66 4.5.2 Connected Components Analysis 67 5 Geometric Transformations 71 5.1 Problem Specification and Algorithm 71 5.2 Affine Transformations 73 5.2.1 Known Affine Transformations 74 5.2.2 Unknown Affine Transformations 75 5.3 Perspective Transformations 76 5.4 Specification of More Complex Transformations 78 5.5 Interpolation 78 5.5.1 Nearest Neighbour Interpolation 79 5.5.2 Bilinear Interpolation 79 5.5.3 Bi-Cubic Interpolation 80 5.6 Modelling and Removing Distortion from Cameras 80 5.6.1 Camera Distortions 81 5.6.2 Camera Calibration and Removing Distortion 82 6 Edges 83 6.1 Edge Detection 83 6.1.1 First Derivative Edge Detectors 85 6.1.2 Second Derivative Edge Detectors 92 6.1.3 Multispectral Edge Detection 97 6.1.4 Image Sharpening 98 6.2 Contour Segmentation 99 6.2.1 Basic Representations of Edge Data 99 6.2.2 Border Detection 102 6.2.3 Extracting Line Segment Representations of Edge Contours 105 6.3 Hough Transform 108 6.3.1 Hough for Lines 109 6.3.2 Hough for Circles 111 6.3.3 Generalised Hough 112 7 Features 115 7.1 Moravec Corner Detection 117 7.2 Harris Corner Detection 118 7.3 FAST Corner Detection 121 7.4 SIFT 122 7.4.1 Scale Space Extrema Detection 123 7.4.2 Accurate Keypoint Location 124 7.4.3 Keypoint Orientation Assignment 126 7.4.4 Keypoint Descriptor 127 7.4.5 Matching Keypoints 127 7.4.6 Recognition 127 7.5 Other Detectors 129 7.5.1 Minimum Eigenvalues 130 7.5.2 SURF 130 8 Recognition 131 8.1 Template Matching 131 8.1.1 Applications 131 8.1.2 Template Matching Algorithm 133 8.1.3 Matching Metrics 134 8.1.4 Finding Local Maxima or Minima 135 8.1.5 Control Strategies for Matching 137 8.2 Chamfer Matching 137 8.2.1 Chamfering Algorithm 137 8.2.2 Chamfer Matching Algorithm 139 8.3 Statistical Pattern Recognition 140 8.3.1 Probability Review 142 8.3.2 Sample Features 143 8.3.3 Statistical Pattern Recognition Technique 149 8.4 Cascade of Haar Classifiers 152 8.4.1 Features 154 8.4.2 Training 156 8.4.3 Classifiers 156 8.4.4 Recognition 158 8.5 Other Recognition Techniques 158 8.5.1 Support Vector Machines (SVM) 158 8.5.2 Histogram of Oriented Gradients (HoG) 159 8.6 Performance 160 8.6.1 Image and Video Datasets 160 8.6.2 Ground Truth 161 8.6.3 Metrics for Assessing Classification Performance 162 8.6.4 Improving Computation Time 165 9 Video 167 9.1 Moving Object Detection 167 9.1.1 Object of Interest 168 9.1.2 Common Problems 168 9.1.3 Difference Images 169 9.1.4 Background Models 171 9.1.5 Shadow Detection 179 9.2 Tracking 180 9.2.1 Exhaustive Search 181 9.2.2 Mean Shift 181 9.2.3 Dense Optical Flow 182 9.2.4 Feature Based Optical Flow 185 9.3 Performance 186 9.3.1 Video Datasets (and Formats) 186 9.3.2 Metrics for Assessing Video Tracking Performance 187 10 Vision Problems 189 10.1 Baby Food 189 10.2 Labels on Glue 190 10.3 O-rings 191 10.4 Staying in Lane 192 10.5 Reading Notices 193 10.6 Mailboxes 194 10.7 Abandoned and Removed Object Detection 195 10.8 Surveillance 196 10.9 Traffic Lights 197 10.10 Real Time Face Tracking 198 10.11 Playing Pool 199 10.12 Open Windows 200 10.13 Modelling Doors 201 10.14 Determining the Time from Analogue Clocks 202 10.15 Which Page 203 10.16 Nut/Bolt/Washer Classification 204 10.17 Road Sign Recognition 205 10.18 License Plates 206 10.19 Counting Bicycles 207 10.20 Recognise Paintings 208 References 209 Index 213
£44.60
John Wiley & Sons Inc Computer Vision in Vehicle Technology
Book SynopsisComputer Vision in Vehicle Technology: Land, Sea & Air Antonio M. Lopez, Universitat Autonoma de Barcelona, Spain Atsushi Imiya, Chiba University, Japan Tomas Pajdla, Czech Technical University, Prague Jose M.Table of ContentsList of Contributors ix Preface xi Abbreviations and Acronyms xiii 1 Computer Vision in Vehicles 1Reinhard Klette 1.1 Adaptive Computer Vision for Vehicles 1 1.1.1 Applications 1 1.1.2 Traffic Safety and Comfort 2 1.1.3 Strengths of (Computer) Vision 2 1.1.4 Generic and Specific Tasks 3 1.1.5 Multi-module Solutions 4 1.1.6 Accuracy, Precision, and Robustness 5 1.1.7 Comparative Performance Evaluation 5 1.1.8 There Are Many Winners 6 1.2 Notation and Basic Definitions 6 1.2.1 Images and Videos 6 1.2.2 Cameras 8 1.2.3 Optimization 10 1.3 Visual Tasks 12 1.3.1 Distance 12 1.3.2 Motion 16 1.3.3 Object Detection and Tracking 18 1.3.4 Semantic Segmentation 21 1.4 Concluding Remarks 23 Acknowledgments 23 2 Autonomous Driving 24Uwe Franke 2.1 Introduction 24 2.1.1 The Dream 24 2.1.2 Applications 25 2.1.3 Level of Automation 26 2.1.4 Important Research Projects 27 2.1.5 Outdoor Vision Challenges 30 2.2 Autonomous Driving in Cities 31 2.2.1 Localization 33 2.2.2 Stereo Vision-Based Perception in 3D 36 2.2.3 Object Recognition 43 2.3 Challenges 49 2.3.1 Increasing Robustness 49 2.3.2 Scene Labeling 50 2.3.3 Intention Recognition 52 2.4 Summary 52 Acknowledgments 54 3 Computer Vision for MAVs 55Friedrich Fraundorfer 3.1 Introduction 55 3.2 System and Sensors 57 3.3 Ego-Motion Estimation 58 3.3.1 State Estimation Using Inertial and Vision Measurements 58 3.3.2 MAV Pose from Monocular Vision 62 3.3.3 MAV Pose from Stereo Vision 63 3.3.4 MAV Pose from Optical Flow Measurements 65 3.4 3D Mapping 67 3.5 Autonomous Navigation 71 3.6 Scene Interpretation 72 3.7 Concluding Remarks 73 4 Exploring the Seafloor with Underwater Robots 75Rafael Garcia, Nuno Gracias, Tudor Nicosevici, Ricard Prados, Natalia Hurtos, Ricard Campos, Javier Escartin, Armagan Elibol, Ramon Hegedus and Laszlo Neumann 4.1 Introduction 75 4.2 Challenges of Underwater Imaging 77 4.3 Online Computer Vision Techniques 79 4.3.1 Dehazing 79 4.3.2 Visual Odometry 84 4.3.3 SLAM 87 4.3.4 Laser Scanning 91 4.4 Acoustic Imaging Techniques 92 4.4.1 Image Formation 92 4.4.2 Online Techniques for Acoustic Processing 95 4.5 Concluding Remarks 98 Acknowledgments 99 5 Vision-Based Advanced Driver Assistance Systems 100David Gerónimo, David Vázquez and Arturo de la Escalera 5.1 Introduction 100 5.2 Forward Assistance 101 5.2.1 Adaptive Cruise Control (ACC) and Forward Collision Avoidance (FCA) 101 5.2.2 Traffic Sign Recognition (TSR) 103 5.2.3 Traffic Jam Assist (TJA) 105 5.2.4 Vulnerable Road User Protection 106 5.2.5 Intelligent Headlamp Control 109 5.2.6 Enhanced Night Vision (Dynamic Light Spot) 110 5.2.7 Intelligent Active Suspension 111 5.3 Lateral Assistance 112 5.3.1 Lane Departure Warning (LDW) and Lane Keeping System (LKS) 112 5.3.2 Lane Change Assistance (LCA) 115 5.3.3 Parking Assistance 116 5.4 Inside Assistance 117 5.4.1 Driver Monitoring and Drowsiness Detection 117 5.5 Conclusions and Future Challenges 119 5.5.1 Robustness 119 5.5.2 Cost 121 Acknowledgments 121 6 Application Challenges from a Bird’s-Eye View 122Davide Scaramuzza 6.1 Introduction to Micro Aerial Vehicles (MAVs) 122 6.1.1 Micro Aerial Vehicles (MAVs) 122 6.1.2 Rotorcraft MAVs 123 6.2 GPS-Denied Navigation 124 6.2.1 Autonomous Navigation with Range Sensors 124 6.2.2 Autonomous Navigation with Vision Sensors 125 6.2.3 SFLY: Swarm of Micro Flying Robots 126 6.2.4 SVO, a Visual-Odometry Algorithm for MAVs 126 6.3 Applications and Challenges 127 6.3.1 Applications 127 6.3.2 Safety and Robustness 128 6.4 Conclusions 132 7 Application Challenges of Underwater Vision 133Nuno Gracias, Rafael Garcia, Ricard Campos, Natalia Hurtos, Ricard Prados, ASM Shihavuddin, Tudor Nicosevici, Armagan Elibol, Laszlo Neumann and Javier Escartin 7.1 Introduction 133 7.2 Offline Computer Vision Techniques for Underwater Mapping and Inspection 134 7.2.1 2D Mosaicing 134 7.2.2 2.5D Mapping 144 7.2.3 3D Mapping 146 7.2.4 Machine Learning for Seafloor Classification 154 7.3 Acoustic Mapping Techniques 157 7.4 Concluding Remarks 159 8 Closing Notes 161Antonio M. López References 164 Index 195
£67.46
John Wiley & Sons Inc Machine Learning Applications
Book SynopsisMachine Learning Applications Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader's active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space explor
£88.65
ISTE Ltd Change Detection and Image Time-Series Analysis
Book SynopsisChange Detection and Image Time Series Analysis 1 presents a wide range of unsupervised methods for temporal evolution analysis through the use of image time series associated with optical and/or synthetic aperture radar acquisition modalities. Chapter 1 introduces two unsupervised approaches to multiple-change detection in bi-temporal multivariate images, with Chapters 2 and 3 addressing change detection in image time series in the context of the statistical analysis of covariance matrices. Chapter 4 focuses on wavelets and convolutional-neural filters for feature extraction and entropy-based anomaly detection, and Chapter 5 deals with a number of metrics such as cross correlation ratios and the Hausdorff distance for variational analysis of the state of snow. Chapter 6 presents a fractional dynamic stochastic field model for spatio temporal forecasting and for monitoring fast-moving meteorological events such as cyclones. Chapter 7 proposes an analysis based on characteristic points for texture modeling, in the context of graph theory, and Chapter 8 focuses on detecting new land cover types by classification-based change detection or feature/pixel based change detection. Chapter 9 focuses on the modeling of classes in the difference image and derives a multiclass model for this difference image in the context of change vector analysis.Table of ContentsContents Preface xi Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE List of Notations Chapter 1 Unsupervised Change Detection in Multitemporal Remote Sensing Images 1 Sicong LIU, Francesca BOVOLO, Lorenzo BRUZZONE, QianDU and Xiaohua TONG 1.1. Introduction 1 1.2. Unsupervised change detection in multispectral images 3 1.2.1.Relatedconcepts 3 1.2.2.Openissuesandchallenges 7 1.2.3. Spectral–spatial unsupervised CD techniques 7 1.3 Unsupervised multiclass change detection approaches based on modelingspectral–spatialinformation 9 1.3.1 Sequential spectral change vector analysis (S 2 CVA) 9 1.3.2. Multiscale morphological compressed change vector analysis 11 1.3.3. Superpixel-level compressed change vector analysis 15 1.4.Datasetdescriptionandexperimentalsetup 18 1.4.1.Datasetdescription 18 1.4.2.Experimentalsetup 22 1.5.Resultsanddiscussion 24 1.5.1.ResultsontheXuzhoudataset 24 1.5.2. Results on the Indonesia tsunami dataset 24 xv 1.6.Conclusion 28 1.7.Acknowledgements 29 1.8.References 29 Chapter 2 Change Detection in Time Series of Polarimetric SAR Images 35 Knut CONRADSEN, Henning SKRIVER, MortonJ.CANTY andAllanA.NIELSEN 2.1. Introduction 35 2.1.1.Theproblem 36 2.1.2 Important concepts illustrated by means of the gamma distribution 39 2.2.Testtheoryandmatrixordering 45 2.2.1. Test for equality of two complex Wishart distributions 45 2.2.2. Test for equality of k-complex Wishart distributions 47 2.2.3. The block diagonal case 49 2.2.4.TheLoewnerorder 52 2.3.Thebasicchangedetectionalgorithm 53 2.4.Applications 55 2.4.1.Visualizingchanges 58 2.4.2.Fieldwisechangedetection 59 2.4.3. Directional changes using the Loewner ordering 62 2.4.4. Software availability 65 2.5.References 70 Chapter 3 An Overview of Covariance-based Change Detection Methodologies in Multivariate SAR Image Time Series 73 Ammar MIAN, Guillaume GINOLHAC, Jean-Philippe OVARLEZ, Arnaud BRELOY and Frédéric PASCAL 3.1. Introduction 73 3.2.Datasetdescription 76 3.3.StatisticalmodelingofSARimages 77 3.3.1.Thedata 77 3.3.2.Gaussianmodel 77 3.3.3.Non-Gaussianmodeling 83 3.4.Dissimilaritymeasures 84 3.4.1.Problemformulation 84 3.4.2. Hypothesis testing statistics 85 3.4.3.Information-theoreticmeasures 87 3.4.4.Riemanniangeometrydistances 89 3.4.5.Optimaltransport 90 3.4.6.Summary 91 3.4.7. Results of change detectors on the UAVSAR dataset 91 3.5. Change detection based on structured covariances 94 3.5.1. Low-rank Gaussian change detector 96 3.5.2. Low-rank compound Gaussian change detector 97 3.5.3. Results of low-rank change detectors on the UAVSAR dataset 100 3.6.Conclusion 102 3.7.References 103 Chapter 4 Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy 109 Abdourrahmane M. ATTO, Fatima KARBOU, Sophie GIFFARD-ROISIN and Lionel BOMBRUN 4.1. Introduction 109 4.2.Parametricmodelingofconvnetfeatures 110 4.3.Anomalydetectioninimagetimeseries 113 4.4.Functionalimagetimeseriesclustering 119 4.5.Conclusion 123 4.6.References 123 Chapter 5 Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series 127 Fatima KARBOU, Guillaume JAMES, Philippe DURAND and Abdourrahmane M. ATTO 5.1. Introduction 127 5.2.Testareaanddata 129 5.3.WetsnowdetectionusingSentinel-1 129 5.4.Metricstodetectwetsnow 133 5.5.Discussion 138 5.6.Conclusion 143 5.7.Acknowledgements 143 5.8.References 143 Chapter 6 Fractional Field Image Time Series Modeling and Application to Cyclone Tracking 145 Abdourrahmane M. ATTO, Aluísio PINHEIRO, Guillaume GINOLHAC and Pedro MORETTIN 6.1. Introduction 145 6.2. Random field model of a cyclone texture 148 6.2.1.Cyclonetexturefeature 149 6.2.2. Wavelet-based power spectral densities and cyclone fields 150 6.2.3. Fractional spectral power decay model 153 6.3.Cyclonefieldeyedetectionandtracking 157 6.3.1.Cycloneeyedetection 157 6.3.2.Dynamicfractalfieldeyetracking 158 6.4. Cyclone field intensity evolution prediction 159 6.5.Discussion 161 6.6.Acknowledgements 163 6.7.References 163 Chapter 7 Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Images 167 Minh-Tan PHAM and Grégoire MERCIER 7.1. Introduction 167 7.2. Texture representation and characterization using local extrema 169 7.2.1.Motivationandapproach 169 7.2.2. Local extrema keypoints within SAR images 172 7.3.Unsupervisedchangedetection 175 7.3.1. Proposed framework 175 7.3.2. Weighted graph construction from keypoints 176 7.3.3.Changemeasure(CM)generation 178 7.4.Experimentalstudy 179 7.4.1. Data description and evaluation criteria 179 7.4.2.Changedetectionresults 181 7.4.3.Sensitivitytoparameters 185 7.4.4.ComparisonwiththeNLMmodel 188 7.4.5. Analysis of the algorithm complexity 191 7.5.Applicationtoglacierflowmeasurement 192 7.5.1. Proposed method 193 7.5.2.Results 194 7.6.Conclusion 196 7.7.References 197 Chapter 8 Multitemporal Analysis of Sentinel-1/2 Images for Land Use Monitoring at Regional Scale 201 Andrea GARZELLI and Claudia ZOPPETTI 8.1. Introduction 201 8.2. Proposed method 203 8.2.1.Testsiteanddata 206 8.3.SARprocessing 209 8.4.Opticalprocessing 215 8.5.Combinationlayer 217 8.6.Results 219 8.7.Conclusion 220 8.8.References 221 Chapter 9 Statistical Difference Models for Change Detection in Multispectral Images 223 Massimo ZANETTI, Francesca BOVOLO and Lorenzo BRUZZONE 9.1. Introduction 223 9.2. Overview of the change detection problem 225 9.2.1. Change detection methods for multispectral images 227 9.2.2. Challenges addressed in this chapter 230 9.3 The Rayleigh–Rice mixture model for the magnitude of the differenceimage 231 9.3.1. Magnitude image statistical mixture model 231 9.3.2.Bayesiandecision 233 9.3.3. Numerical approach to parameter estimation 234 9.4. A compound multiclass statistical model of the difference image 239 9.4.1. Difference image statistical mixture model 240 9.4.2. Magnitude image statistical mixture model 245 9.4.3.Bayesiandecision 248 9.4.4. Numerical approach to parameter estimation 249 9.5.Experimentalresults 253 9.5.1.Datasetdescription 253 9.5.2.Experimentalsetup 256 9.5.3. Test 1: Two-class Rayleigh–Rice mixture model 256 9.5.4. Test 2: Multiclass Rician mixture model 260 9.6.Conclusion 266 9.7.References 267 List of Authors 275 Index 277 Summary of Volume 2 281
£124.15
ISTE Ltd Change Detection and Image Time Series Analysis
Book SynopsisChange Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series.Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches.Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations,Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.Table of ContentsContents Preface ix Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE List of Notations Chapter 1 Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series 1 Ihsen HEDHLI, Gabriele MOSER, Sebastiano B. SERPICO and Josiane ZERUBIA 1.1. Introduction 1 1.1.1. The role of multisensor data in time series classification 1 1.1.2. Multisensor and multiresolution classification 2 1.1.3.Previouswork 5 1.2. Methodology 9 1.2.1. Overview of the proposed approaches 9 1.2.2. Hierarchical model associated with the first proposed method 10 1.2.3. Hierarchical model associated with the second proposed method 13 1.2.4. Multisensor hierarchical MPM inference 14 1.2.5. Probability density estimation through finite mixtures 17 1.3.Examplesofexperimentalresults 19 1.3.1.Resultsofthefirstmethod 19 1.3.2.Resultsofthesecondmethod 22 1.4.Conclusion 26 xiii 1.5.Acknowledgments 26 1.6.References 27 Chapter 2 Pixel-based Classification Techniques for Satellite Image Time Series 33 Charlotte PELLETIER and Silvia VALERO 2.1. Introduction 33 2.2. Basic concepts in supervised remote sensing classification 35 2.2.1. Preparing data before it is fed into classification algorithms 35 2.2.2. Key considerations when training supervised classifiers 39 2.2.3. Performance evaluation of supervised classifiers 41 2.3.Traditionalclassificationalgorithms 45 2.3.1. Support vector machines 45 2.3.2. Random forests 51 2.3.3. k-nearest neighbor 56 2.4. Classification strategies based on temporal feature representations 59 2.4.1. Phenology-based classification approaches 60 2.4.2 Dictionary-based classificationapproaches 61 2.4.3 Shapelet-based classificationapproaches 62 2.5.Deeplearningapproaches 63 2.5.1. Introduction to deep learning 64 2.5.2.Convolutionalneuralnetworks 68 2.5.3.Recurrentneuralnetworks 71 2.6.References 75 Chapter 3 Semantic Analysis of Satellite Image Time Series 85 Corneliu Octavian DUMITRU and Mihai DATCU 3.1. Introduction 85 3.1.1.TypicalSITSexamples 89 3.1.2. Irregular acquisitions 90 3.1.3.Thechapterstructure 96 3.2.WhyaresemanticsneededinSITS? 96 3.3.Similaritymetrics 97 3.4. Feature methods 98 3.5. Classification methods 98 3.5.1.Activelearning 99 3.5.2.Relevancefeedback 100 3.5.3. Compression-based pattern recognition 100 3.5.4.LatentDirichletallocation 101 3.6.Conclusion 102 vii 3.7.Acknowledgments 105 3.8.References 105 Chapter 4 Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond 109 Matthieu MOLINIER, Jukka MIETTINEN,DinoIENCO,ShiQIU and Zhe ZHU 4.1. Introduction 109 4.2. Annual time series 111 4.2.1. Overview of annual time series methods 111 4.2.2 Examples of annual times series analysis applications for environmentalmonitoring 112 4.2.3.Towardsdensetimeseriesanalysis 116 4.3. Dense time series analysis using all available data 117 4.3.1. Making dense time series consistent 118 4.3.2. Change detection methods 121 4.3.3.Summaryandfuturedevelopments 125 4.4. Deep learning-based time series analysis approaches 126 4.4.1 Recurrent Neural Network (RNN) for Satellite Image TimeSeries 129 4.4.2 Convolutional Neural Networks (CNN) for Satellite Image TimeSeries 131 4.4.3. Hybrid models: Convolutional Recurrent Neural Network (ConvRNN) models for Satellite Image Time Series 134 4.4.4. Synthesis and future developments 136 4.5. Beyond satellite image time series and deep learning: convergence between time series and video approaches 136 4.5.1 Increased image acquisition frequency: from time series to spacebornetime-lapseandvideos 137 4.5.2. Deep learning and computer vision as technology enablers 138 4.5.3.Futuresteps 139 4.6.References 140 Chapter 5 A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images 155 Gülşen TAŞKIN, EsraERTEN and Enes Oğuzhan ALATAŞ 5.1. Introduction 155 5.1.1. Research methodology and statistics 159 5.2. Satellite-based earthquake damage assessment 165 5.3. Pre-processing of satellite images before damage assessment 167 5.4. Multi-source image analysis 168 5.5. Contextual feature mining for damage assessment 169 5.5.1.Texturalfeatures 170 5.5.2. Filter-based methods 173 5.6. Multi-temporal image analysis for damage assessment 175 5.6.1. Use of machine learning in damage assessment problem 176 5.6.2. Rapid earthquake damage assessment 180 5.7. Understanding damage following an earthquake using satellite-based SAR 181 5.7.1. SAR fundamental parameters and acquisition vector 185 5.7.2. Coherent methods for damage assessment 188 5.7.3. Incoherent methods for damage assessment 192 5.7.4. Post-earthquake-only SAR data-based damage assessment 195 5.7.5 Combination of coherent and incoherent methods for damage assessment 196 5.7.6.Summary 198 5.8. Use of auxiliary data sources 200 5.9.Damagegrades 200 5.10.Conclusionanddiscussion 203 5.11.References 205 Chapter 6 Multiclass Multilabel Change of State Transfer Learning from Image Time Series 223 Abdourrahmane M. ATTO,HélaHADHRI, FlavienVERNIER and Emmanuel TROUVÉ 6.1. Introduction 223 6.2. Coarse- to fine-grained change of state dataset 225 6.3. Deep transfer learning models for change of state classification 232 6.3.1.Deeplearningmodellibrary 232 6.3.2.GraphstructuresfortheCNNlibrary 234 6.3.3. Dimensionalities of the learnables for the CNN library 236 6.4.Changeofstateanalysis 237 6.4.1 Transfer learning adaptations for the change of state classificationissues 238 6.4.2.Experimentalresults 239 6.5.Conclusion 243 6.6.Acknowledgments 244 6.7.References 244 List of Authors 247 Index 249 Summary of Volume 1 253
£124.15
ISTE Ltd Face Analysis Under Uncontrolled Conditions: From
Book SynopsisFace analysis is essential for a large number of applications such as human-computer interaction or multimedia (e.g. content indexing and retrieval). Although many approaches are under investigation, performance under uncontrolled conditions is still not satisfactory. The variations that impact facial appearance (e.g. pose, expression, illumination, occlusion, motion blur) make it a difficult problem to solve.This book describes the progress towards this goal, from a core building block – landmark detection – to the higher level of micro and macro expression recognition. Specifically, the book addresses the modeling of temporal information to coincide with the dynamic nature of the face. It also includes a benchmark of recent solutions along with details about the acquisition of a dataset for such tasks.Table of ContentsPreface xiRomain BELMONTE and Benjamin ALLAERT Part 1. Facial Landmark Detection 1 Introduction to Part 1 3Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA Chapter 1. Facial Landmark Detection 13Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA 1.1. Facial landmark detection in still images 14 1.1.1.Generativeapproaches 14 1.1.2.Discriminative approaches 18 1.1.3.Deep learningapproaches 24 1.1.4.Handlingchallenges 34 1.1.5.Summary 40 1.2.Extendingfacial landmarkdetectionto videos 41 1.2.1.Trackingby detection 41 1.2.2.Box, landmarkand pose tracking 43 1.2.3.Adaptive approaches 45 1.2.4. Joint approaches 46 1.2.5. Temporal constrained approaches 47 1.2.6.Summary 49 1.3.Discussion 50 1.4.References 52 Chapter 2. Effectiveness of Facial Landmark Detection 67Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA 2.1.Overview 68 2.2.Datasets and evaluationmetrics 69 2.2.1. Image and videodatasets 69 2.2.2. Face preprocessing and data augmentation 73 2.2.3.Evaluationmetrics 75 2.2.4.Summary 77 2.3. Image andvideobenchmarks 77 2.3.1. Compiled results on 300W 77 2.3.2. Compiled results on 300VW 79 2.4.Cross-dataset benchmark 80 2.4.1.Evaluationprotocol 80 2.4.2.Comparisonof selected approaches 82 2.5.Discussion 86 2.6.References 88 Chapter 3. Facial Landmark Detection with Spatio-temporal Modeling 93Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA 3.1.Overview 94 3.2.Spatio-temporalmodelingreview 95 3.2.1.Hand-craftedapproaches 95 3.2.2.Deep learningapproaches 97 3.2.3.Summary 103 3.3.Architecturedesign 104 3.3.1. Coordinate regression networks 104 3.3.2.Heatmapregressionnetworks 106 3.4.Experiments 107 3.4.1.Datasets andevaluationprotocols 107 3.4.2. Implementationdetails 108 3.4.3.EvaluationonSNaP-2DFe 109 3.4.4. Evaluation on 300VW 111 3.4.5.Comparisonwith existingmodels 112 3.4.6. Qualitative results 112 3.4.7.Propertiesof the networks 114 3.5.Design investigations 114 3.5.1.Encoder-decoder 115 3.5.2. Complementarity between spatial and temporal information 117 3.5.3. Complementarity between local and global motion 119 3.6.Discussion 122 3.7.References 123 Conclusion to Part 1 133Romain BELMONTE, Pierre TIRILLY, IoanMarius BILASCO, Nacim IHADDADENE and Chaabane DJERABA Part 2. Facial Expression Analysis 147 Introduction to Part 2 149Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA Chapter 4. Extraction of Facial Features 157Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 4.1. Introduction 157 4.2.Face detection 158 4.2.1.Point-of-interestdetectionalgorithms 160 4.2.2.Face alignment approaches 162 4.2.3.Synthesis 166 4.3.Face normalization 166 4.3.1.Dealingwith headpose variations 167 4.3.2.Dealingwith facial occlusions 170 4.3.3.Synthesis 172 4.4.Extractionof visual features 172 4.4.1.Facial appearancefeatures 172 4.4.2.Facial geometric features 174 4.4.3. Facial dynamics features 175 4.4.4.Facial segmentationmodels 177 4.4.5.Synthesis 179 4.5. Learning methods 179 4.5.1.Classification versus regression 180 4.5.2.Fusionmodel 182 4.5.3.Synthesis 184 4.6.Conclusion 185 4.7.References 186 Chapter 5. Facial Expression Modeling 191Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 5.1. Introduction 191 5.2.Modelingof the affective state 192 5.2.1.Categoricalmodeling 192 5.2.2.Dimensionalmodeling 194 5.2.3.Synthesis 196 5.3. The challenges of facial expression recognition 197 5.3.1. The variation of the intensity of the expressions 197 5.3.2.Variationof facialmovement 199 5.3.3.Synthesis 200 5.4.The learningdatabases 201 5.4.1. Improvementof learningdata 201 5.4.2. Comparison of learning databases 203 5.4.3.Synthesis 205 5.5. Invariance to facial expression intensities 206 5.5.1.Macro-expression 206 5.5.2.Micro-expression 208 5.5.3.Synthesis 209 5.6. Invarianceto facialmovements 211 5.6.1. Pose variations (PV) and large displacements (LD) 211 5.6.2.Synthesis 214 5.7.Conclusion 215 5.8.References 216 Chapter 6. Facial Motion Characteristics 223Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 6.1. Introduction 223 6.2.Characteristics of the facialmovement 225 6.2.1. Local constraint of magnitude and direction 226 6.2.2. Local constraint of the motion distribution 228 6.2.3.Motionpropagationconstraint 230 6.3.LMP 232 6.3.1. Local consistency of the movement 233 6.3.2.Consistencyof local distribution 236 6.3.3. Coherence in the propagationof themovement 238 6.4.Conclusion 241 6.5.References 242 Chapter 7. Micro- and Macro-Expression Analysis 243Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 7.1. Introduction 243 7.2. Definition of a facial segmentation model 244 7.3.Feature vector construction 247 7.3.1.Motionfeaturesvector 247 7.3.2.Geometric featuresvector 248 7.3.3.Features fusion 249 7.4. Recognition process 250 7.5. Evaluation on micro- and macro-expressions 251 7.5.1.Learningdatabases 252 7.5.2. Micro-expression recognition 253 7.5.3. Macro-expressions recognition 255 7.5.4. Synthesis of experiments on micro- and macro-expressions 258 7.6. Same expression with different intensities 260 7.6.1.Data preparation 260 7.6.2.Fractional time analysis 263 7.6.3.Analysis on a different time frame 264 7.6.4. Synthesis of experiments on activation segments 265 7.7.Conclusion 265 7.8.References 266 Chapter 8. Towards Adaptation to Head Pose Variations 271Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA 8.1. Introduction 271 8.2.Learningdatabase challenges 273 8.3. Innovative acquisition system (SNaP-2DFe) 274 8.4. Evaluation of face normalization methods 276 8.4.1. Does the normalization preserve the facial geometry? 277 8.4.2. Does normalization preserve facial expressions? 280 8.5.Conclusion 283 8.6.References 284 Conclusion to Part 2 287Benjamin ALLAERT, IoanMarius BILASCO and Chaabane DJERABA List of Authors 293 Index 295
£112.50
Springer International Publishing AG Robotics, Vision and Control: Fundamental
Book SynopsisThis textbook provides a comprehensive, but tutorial, introduction to robotics, computer vision, and control. It is written in a light but informative conversational style, weaving text, figures, mathematics, and lines of code into a cohesive narrative. Over 1600 code examples show how complex problems can be decomposed and solved using just a few simple lines of code. This edition is based on MATLAB® and a number of MathWorks® toolboxes. These provide a set of supported software tools for addressing a broad range of applications in robotics and computer vision. These toolboxes enable the reader to easily bring the algorithmic concepts into practice and work with real, non-trivial, problems. For the beginning student, the book makes the algorithms accessible, the toolbox code can be read to gain understanding, and the examples illustrate how it can be used. The code can also be the starting point for new work, for practitioners, students, or researchers, by writing programs based on toolbox functions. Two co-authors from MathWorks have joined the writing team and bring deep knowledge of these MATLAB toolboxes and workflows.Table of ContentsIntroduction.-Foundations: Representing Position and Orientation.- Time and Motion.- Mobile Robotics: Mobile Robot Vehicles.- Navigation.- Localization and Mapping.- Robot Manipulators: Robot Arm Kinematics.- Manipulator Velocity.- Dynamics and Control.- Computer Vision: Light and Color.- Images and Image Processing,- Image Feature Extraction,- Image Formation.- Using Multiple Images.- Installing the Toolboxes.- Linear Algebra.- Geometry.- Lie Groups and Algebras.
£49.49
De Gruyter Variational Methods: In Imaging and Geometric Control
Book SynopsisWith a focus on the interplay between mathematics and applications of imaging, the first part covers topics from optimization, inverse problems and shape spaces to computer vision and computational anatomy. The second part is geared towards geometric control and related topics, including Riemannian geometry, celestial mechanics and quantum control. Contents: Part I Second-order decomposition model for image processing: numerical experimentation Optimizing spatial and tonal data for PDE-based inpainting Image registration using phase・amplitude separation Rotation invariance in exemplar-based image inpainting Convective regularization for optical flow A variational method for quantitative photoacoustic tomography with piecewise constant coefficients On optical flow models for variational motion estimation Bilevel approaches for learning of variational imaging models Part II Non-degenerate forms of the generalized Euler・Lagrange condition for state-constrained optimal control problems The Purcell three-link swimmer: some geometric and numerical aspects related to periodic optimal controls Controllability of Keplerian motion with low-thrust control systems Higher variational equation techniques for the integrability of homogeneous potentials Introduction to KAM theory with a view to celestial mechanics Invariants of contact sub-pseudo-Riemannian structures and Einstein・Weyl geometry Time-optimal control for a perturbed Brockett integrator Twist maps and Arnold diffusion for diffeomorphisms A Hamiltonian approach to sufficiency in optimal control with minimal regularity conditions: Part I Index
£144.40
De Gruyter Topological Optimization and Optimal Transport: In the Applied Sciences
Book SynopsisBy discussing topics such as shape representations, relaxation theory and optimal transport, trends and synergies of mathematical tools required for optimization of geometry and topology of shapes are explored. Furthermore, applications in science and engineering, including economics, social sciences, biology, physics and image processing are covered. Contents Part I Geometric issues in PDE problems related to the infinity Laplace operator Solution of free boundary problems in the presence of geometric uncertainties Distributed and boundary control problems for the semidiscrete Cahn–Hilliard/Navier–Stokes system with nonsmooth Ginzburg–Landau energies High-order topological expansions for Helmholtz problems in 2D On a new phase field model for the approximation of interfacial energies of multiphase systems Optimization of eigenvalues and eigenmodes by using the adjoint method Discrete varifolds and surface approximation Part II Weak Monge–Ampere solutions of the semi-discrete optimal transportation problem Optimal transportation theory with repulsive costs Wardrop equilibria: long-term variant, degenerate anisotropic PDEs and numerical approximations On the Lagrangian branched transport model and the equivalence with its Eulerian formulation On some nonlinear evolution systems which are perturbations of Wasserstein gradient flows Pressureless Euler equations with maximal density constraint: a time-splitting scheme Convergence of a fully discrete variational scheme for a thin-film equatio Interpretation of finite volume discretization schemes for the Fokker–Planck equation as gradient flows for the discrete Wasserstein distance
£144.40
Elsevier Science Advanced Methods and Deep Learning in Computer Vision
Table of Contents List of contributors xi About the editors xiii Preface xv 1. The dramatically changing face of computer vision E.R. DAVIES 1.1 Introduction – computer vision and its origins 1 1.2 Part A – Understanding low-level image processing operators 4 1.3 Part B – 2-D object location and recognition 15 1.4 Part C – 3-D object location and the importance of invariance 29 1.5 Part D – Tracking moving objects 55 1.6 Part E – Texture analysis 61 1.7 Part F – From artificial neural networks to deep learning methods 68 1.8 Part G – Summary 86 References 87 2. Advanced methods for robust object detection ZHAOWEI CAI AND NUNO VASCONCELOS 2.1 Introduction 93 2.2 Preliminaries 95 2.3 R-CNN 96 2.4 SPP-Net 97 2.5 Fast R-CNN 98 2.6 Faster R-CNN 101 2.7 Cascade R-CNN 103 2.8 Multiscale feature representation 106 2.9 YOLO 110 2.10 SSD 112 2.11 RetinaNet 113 2.12 Detection performances 115 2.13 Conclusion 115 References 116 3. Learning with limited supervision SUJOY PAUL AND AMIT K. ROY-CHOWDHURY 3.1 Introduction 119 3.2 Context-aware active learning 120 3.3 Weakly supervised event localization 129 3.4 Domain adaptation of semantic segmentation using weak labels 137 3.5 Weakly-supervised reinforcement learning for dynamical tasks 144 3.6 Conclusions 151 References 153 4. Efficient methods for deep learning HAN CAI, JI LIN, AND SONG HAN 4.1 Model compression 159 4.2 Efficient neural network architectures 170 4.3 Conclusion 185 References 185 5. Deep conditional image generation GANG HUA AND DONGDONG CHEN 5.1 Introduction 191 5.2 Visual pattern learning: a brief review 194 5.3 Classical generative models 195 5.4 Deep generative models 197 5.5 Deep conditional image generation 200 5.6 Disentanglement for controllable synthesis 201 5.7 Conclusion and discussions 216 References 216 6. Deep face recognition using full and partial face images HASSAN UGAIL 6.1 Introduction 221 6.2 Components of deep face recognition 227 6.3 Face recognition using full face images 231 6.4 Deep face recognition using partial face data 233 6.5 Specific model training for full and partial faces 237 6.6 Discussion and conclusions 239 References 240 7. Unsupervised domain adaptation using shallow and deep representations YOGESH BALAJI, HIEN NGUYEN, AND RAMA CHELLAPPA 7.1 Introduction 243 7.2 Unsupervised domain adaptation using manifolds 244 7.3 Unsupervised domain adaptation using dictionaries 247 7.4 Unsupervised domain adaptation using deep networks 258 7.5 Summary 270 References 270 8. Domain adaptation and continual learning in semantic segmentation UMBERTO MICHIELI, MARCO TOLDO, AND PIETRO ZANUTTIGH 8.1 Introduction 275 8.2 Unsupervised domain adaptation 277 8.3 Continual learning 291 8.4 Conclusion 298 References 299 9. Visual tracking MICHAEL FELSBERG 9.1 Introduction 305 9.2 Template-based methods 308 9.3 Online-learning-based methods 314 9.4 Deep learning-based methods 323 9.5 The transition from tracking to segmentation 327 9.6 Conclusions 331 References 332 10. Long-term deep object tracking EFSTRATIOS GAVVES AND DEEPAK GUPTA 10.1 Introduction 337 10.2 Short-term visual object tracking 341 10.3 Long-term visual object tracking 345 10.4 Discussion 367 References 368 11. Learning for action-based scene understanding CORNELIA FERMÜLLER AND MICHAEL MAYNORD 11.1 Introduction 373 11.2 Affordances of objects 375 11.3 Functional parsing of manipulation actions 383 11.4 Functional scene understanding through deep learning with language and vision 390 11.5 Future directions 397 11.6 Conclusions 399 References 399 12. Self-supervised temporal event segmentation inspired by cognitive theories RAMY MOUNIR, SATHYANARAYANAN AAKUR, AND SUDEEP SARKAR 12.1 Introduction 406 12.2 The event segmentation theory from cognitive science 408 12.3 Version 1: single-pass temporal segmentation using prediction 410 12.4 Version 2: segmentation using attention-based event models 421 12.5 Version 3: spatio-temporal localization using prediction loss map 428 12.6 Other event segmentation approaches in computer vision 440 12.7 Conclusions 443 References 444 13. Probabilistic anomaly detection methods using learned models from time-series data for multimedia self-aware systems CARLO REGAZZONI, ALI KRAYANI, GIULIA SLAVIC, AND LUCIO MARCENARO 13.1 Introduction 450 13.2 Base concepts and state of the art 451 13.3 Framework for computing anomaly in self-aware systems 458 13.4 Case study results: anomaly detection on multisensory data from a self-aware vehicle 467 13.5 Conclusions 476 References 477 14. Deep plug-and-play and deep unfolding methods for image restoration KAI ZHANG AND RADU TIMOFTE 14.1 Introduction 481 14.2 Half quadratic splitting (HQS) algorithm 484 14.3 Deep plug-and-play image restoration 485 14.4 Deep unfolding image restoration 492 14.5 Experiments 495 14.6 Discussion and conclusions 504 References 505 15. Visual adversarial attacks and defenses CHANGJAE OH, ALESSIO XOMPERO, AND ANDREA CAVALLARO 15.1 Introduction 511 15.2 Problem definition 512 15.3 Properties of an adversarial attack 514 15.4 Types of perturbations 515 15.5 Attack scenarios 515 15.6 Image processing 522 15.7 Image classification 523 15.8 Semantic segmentation and object detection 529 15.9 Object tracking 529 15.10 Video classification 531 15.11 Defenses against adversarial attacks 533 15.12 Conclusions 537 References 538 Index 545
£112.74
Cambridge University Press Affine Analysis of Image Sequences
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£43.30
Cambridge University Press Visual Motion of Curves and Surfaces
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Cambridge University Press Correlation Pattern Recognition
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Cambridge University Press RealTime Computer Vision 4 Publications of the Newton Institute Series Number 4
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Cambridge University Press Correlation Pattern Recognition
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